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weipengOO98
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403238c9ee | |
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weipengOO98
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6267eda37f | |
 p94628173
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9f956ede62 |
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@ -18,11 +18,11 @@ sys.path.insert(0, os.path.abspath(".."))
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# -- Project information -----------------------------------------------------
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project = "idrlnet"
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copyright = "2021, IDRL"
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copyright = "2023, IDRL"
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author = "IDRL"
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# The full version, including alpha/beta/rc tags
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release = "0.1.0"
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release = "2.0.0-rc3"
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# -- General configuration ---------------------------------------------------
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@ -0,0 +1,123 @@
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# Deepritz
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This section repeats the Deepritz method presented by [Weinan E and Bing Yu](https://link.springer.com/article/10.1007/s40304-018-0127-z).
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Consider the 2d Poisson's equation such as the following:
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$$
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\begin{equation}
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\begin{aligned}
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-\Delta u=f, & \text { in } \Omega \\
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u=0, & \text { on } \partial \Omega
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\end{aligned}
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\end{equation}
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$$
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Based on the scattering theorem, its weak form is that both sides are multiplied by$ v \in H_0^1$(which can be interpreted as some function bounded by 0),to get
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$$
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\int f v=-\int v \Delta u=(\nabla u, \nabla v)
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$$
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The above equation holds for any $v \in H_0^1$. The bilinear part of the right-hand side of the equation with respect to $u,v$ is symmetric and yields the bilinear term:
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$$
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a(u, v)=\int \nabla u \cdot \nabla v
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$$
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By the Poincaré inequality, the $a(\cdot, \cdot)$ is a positive definite operator. By positive definite, we mean that there exists $\alpha >0$, such that
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$$
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a(u, u) \geq \alpha\|u\|^2, \quad \forall u \in H_0^1
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$$
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The remaining term is a linear generalization of $v$, which is $l(v)$, which yields the equation:
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$$
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a(u, v) = l(v)
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$$
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For this equation, by discretizing $u,v$ in the same finite dimensional subspace, we can obtain a symmetric positive definite system of equations, which is the family of Galerkin methods, or we can transform it into a polarization problem to solve it.
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To find $u$ satisfies
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$$
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a(u, v) = l(v), \quad \forall v \in H_0^1
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$$
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For a symmetric positive definite $a$ , which is equivalent to solving the variational minimization problem, that is, finding $u$, such that holds, where
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$$
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J(u) = \frac{1}{2} a(u, u) - l(u)
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$$
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Specifically
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$$
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\min _{u \in H_0^1} J(u)=\frac{1}{2} \int\|\nabla u\|_2^2-\int f v
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$$
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The DeepRitz method is similar to the PINN approach, replacing the neural network with u, and after sampling the region, just solve it with a solver like Adam. Written as
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$$
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\begin{equation}
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\min _{\left.\hat{u}\right|_{\partial \Omega}=0} \hat{J}(\hat{u})=\frac{1}{2} \frac{S_{\Omega}}{N_{\Omega}} \sum\left\|\nabla \hat{u}\left(x_i, y_i\right)\right\|_2^2-\frac{S_{\Omega}}{N_{\partial \Omega}} \sum f\left(x_i, y_i\right) \hat{u}\left(x_i, y_i\right)
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\end{equation}
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$$
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Note that the original $u \in H_0^1$, which is zero on the boundary, is transformed into an unconstrained problem by adding the penalty function term:
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$$
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\begin{equation}
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\begin{gathered}
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\min \hat{J}(\hat{u})=\frac{1}{2} \frac{S_{\Omega}}{N_{\Omega}} \sum\left\|\nabla \hat{u}\left(x_i, y_i\right)\right\|_2^2-\frac{S_{\Omega}}{N_{\Omega}} \sum f\left(x_i, y_i\right) \hat{u}\left(x_i, y_i\right)+\beta \frac{S_{\partial \Omega}}{N_{\partial \Omega}} \\
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\sum \hat{u}^2\left(x_i, y_i\right)
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\end{gathered}
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\end{equation}
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$$
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Consider the 2d Poisson's equation defined on $\Omega=[-1,1]\times[-1,1]$, which satisfies $f=2 \pi^2 \sin (\pi x) \sin (\pi y)$.
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### Define Sampling Methods and Constraints
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For the problem, boundary condition and PDE constraint are presented and use the Identity loss.
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```python
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@sc.datanode(sigma=1000.0)
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class Boundary(sc.SampleDomain):
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def __init__(self):
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self.points = geo.sample_boundary(100,)
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self.constraints = {"u": 0.}
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def sampling(self, *args, **kwargs):
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return self.points, self.constraints
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@sc.datanode(loss_fn="Identity")
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class Interior(sc.SampleDomain):
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def __init__(self):
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self.points = geo.sample_interior(1000)
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self.constraints = {"integral_dxdy": 0,}
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def sampling(self, *args, **kwargs):
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return self.points, self.constraints
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```
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### Define Neural Networks and PDEs
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In the PDE definition section, based on the DeepRitz method we add two types of PDE nodes:
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```python
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def f(x, y):
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return 2 * sp.pi ** 2 * sp.sin(sp.pi * x) * sp.sin(sp.pi * y)
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dx_exp = sc.ExpressionNode(
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expression=0.5*(u.diff(x) ** 2 + u.diff(y) ** 2) - u * f(x, y), name="dxdy"
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)
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net = sc.get_net_node(inputs=("x", "y"), outputs=("u",), name="net", arch=sc.Arch.mlp)
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integral = sc.ICNode("dxdy", dim=2, time=False)
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```
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The result is shown as follows:
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@ -0,0 +1,227 @@
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# Navier-Stokes equations
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This section repeats the Robust PINN method presented by [Peng et.al](https://deepai.org/publication/robust-regression-with-highly-corrupted-data-via-physics-informed-neural-networks).
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## Steady 2D NS equations
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The prototype problem of incompressible flow past a circular cylinder is considered.
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The velocity vector is set to zero at all walls and the pressure is set to p = 0 at the outlet. The fluid density is taken as $\rho = 1kg/m^3$ and the dynamic viscosity is taken as $\mu = 2 · 10^{−2}kg/m^3$ . The velocity profile on the inlet is set as $u(0, y)=4 \frac{U_M}{H^2}(H-y) y$ with $U_M = 1m/s$ and $H = 0.41m$.
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The two-dimensional steady-state Navier-Stokes equation is equivalently transformed into the following equations:
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$$
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\begin{equation}
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\begin{aligned}
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\sigma^{11} &=-p+2 \mu u_x \\
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\sigma^{22} &=-p+2 \mu v_y \\
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\sigma^{12} &=\mu\left(u_y+v_x\right) \\
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p &=-\frac{1}{2}\left(\sigma^{11}+\sigma^{22}\right) \\
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\left(u u_x+v u_y\right) &=\mu\left(\sigma_x^{11}+\sigma_y^{12}\right) \\
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\left(u v_x+v v_y\right) &=\mu\left(\sigma_x^{12}+\sigma_y^{22}\right)
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\end{aligned}
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\end{equation}
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$$
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We construct a neural network with six outputs to satisfy the PDE constraints above:
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$$
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\begin{equation}
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u, v, p, \sigma^{11}, \sigma^{12}, \sigma^{22}=net(x, y)
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\end{equation}
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$$
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### Define Symbols and Geometric Objects
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For the 2d problem, we define two coordinate symbols`x`and`y`, six variables$ u, v, p, \sigma^{11}, \sigma^{12}, \sigma^{22}$ are defined.
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The geometry object is a simple rectangle and circle with the operator `-`.
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```python
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x = Symbol('x')
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y = Symbol('y')
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rec = sc.Rectangle((0., 0.), (1.1, 0.41))
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cir = sc.Circle((0.2, 0.2), 0.05)
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geo = rec - cir
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u = sp.Function('u')(x, y)
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v = sp.Function('v')(x, y)
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p = sp.Function('p')(x, y)
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s11 = sp.Function('s11')(x, y)
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s22 = sp.Function('s22')(x, y)
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s12 = sp.Function('s12')(x, y)
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```
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### Define Sampling Methods and Constraints
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For the problem, three boundary conditions , PDE constraint and external data are presented. We use the robust-PINN model inspired by the traditional LAD (Least Absolute Derivation) approach, where the L1 loss replaces the squared L2 data loss.
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```python
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@sc.datanode
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class Inlet(sc.SampleDomain):
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def sampling(self, *args, **kwargs):
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points = rec.sample_boundary(1000, sieve=(sp.Eq(x, 0.)))
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constraints = sc.Variables({'u': 4 * (0.41 - y) * y / (0.41 * 0.41)})
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return points, constraints
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@sc.datanode
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class Outlet(sc.SampleDomain):
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def sampling(self, *args, **kwargs):
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points = geo.sample_boundary(1000, sieve=(sp.Eq(x, 1.1)))
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constraints = sc.Variables({'p': 0.})
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return points, constraints
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@sc.datanode
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class Wall(sc.SampleDomain):
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def sampling(self, *args, **kwargs):
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points = geo.sample_boundary(1000, sieve=((x > 0.) & (x < 1.1)))
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#print("points3", points)
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constraints = sc.Variables({'u': 0., 'v': 0.})
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return points, constraints
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@sc.datanode(name='NS_external')
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class Interior_domain(sc.SampleDomain):
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def __init__(self):
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self.density = 2000
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def sampling(self, *args, **kwargs):
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points = geo.sample_interior(2000)
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constraints = {'f_s11': 0., 'f_s22': 0., 'f_s12': 0., 'f_u': 0., 'f_v': 0., 'f_p': 0.}
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return points, constraints
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@sc.datanode(name='NS_domain', loss_fn='L1')
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class NSExternal(sc.SampleDomain):
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def __init__(self):
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points = pd.read_csv('NSexternel_sample.csv')
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self.points = {col: points[col].to_numpy().reshape(-1, 1) for col in points.columns}
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self.constraints = {'u': self.points.pop('u'), 'v': self.points.pop('v'), 'p': self.points.pop('p')}
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def sampling(self, *args, **kwargs):
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return self.points, self.constraints
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```
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### Define Neural Networks and PDEs
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In the PDE definition part, we add these PDE nodes:
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```python
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net = sc.MLP([2, 40, 40, 40, 40, 40, 40, 40, 40, 6], activation=sc.Activation.tanh)
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net = sc.get_net_node(inputs=('x', 'y'), outputs=('u', 'v', 'p', 's11', 's22', 's12'), name='net', arch=sc.Arch.mlp)
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pde1 = sc.ExpressionNode(name='f_s11', expression=-p + 2 * nu * u.diff(x) - s11)
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pde2 = sc.ExpressionNode(name='f_s22', expression=-p + 2 * nu * v.diff(y) - s22)
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pde3 = sc.ExpressionNode(name='f_s12', expression=nu * (u.diff(y) + v.diff(x)) - s12)
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pde4 = sc.ExpressionNode(name='f_u', expression=u * u.diff(x) + v * u.diff(y) - nu * (s11.diff(x) + s12.diff(y)))
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pde5 = sc.ExpressionNode(name='f_v', expression=u * v.diff(x) + v * v.diff(y) - nu * (s12.diff(x) + s22.diff(y)))
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pde6 = sc.ExpressionNode(name='f_p', expression=p + (s11 + s22) / 2)
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```
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### Define A Solver
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Direct use of Adam optimization is less effective, so the LBFGS optimization method or a combination of both (Adam+LBFGS) is used for training:
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```python
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s = sc.Solver(sample_domains=(Inlet(), Outlet(), Wall(), Interior_domain(), NSExternal()),
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netnodes=[net],
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init_network_dirs=['network_dir_adam'],
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pdes=[pde1, pde2, pde3, pde4, pde5, pde6],
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max_iter=300,
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opt_config = dict(optimizer='LBFGS', lr=1)
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)
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```
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The result is shown as follows:
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## Unsteady 2D N-S equations with unknown parameters
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A two-dimensional incompressible flow and dynamic vortex shedding past a circular cylinder in a steady-state are numerically simulated. Respectively, the Reynolds number of the incompressible flow is $Re = 100$. The kinematic viscosity of the fluid is $\nu = 0.01$. The cylinder diameter D is 1. The simulation domain size is
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$[-15,25] × [-8,8]$. The computational domain is much smaller: $[1,8] × [-2,2]× [0,20]$.
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$$
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\begin{equation}
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\begin{aligned}
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&u_t+\lambda_1\left(u u_x+v u_y\right)=-p_x+\lambda_2\left(u_{x x}+u_{y y}\right) \\
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&v_t+\lambda_1\left(u v_x+v v_y\right)=-p_y+\lambda_2\left(v_{x x}+v_{y y}\right)
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\end{aligned}
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\end{equation}
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$$
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where $\lambda_1$ and $\lambda_2$ are two unknown parameters to be recovered. We make the assumption that $u=\psi_y, \quad v=-\psi_x$
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for some stream function $\psi(x, y)$. Under this assumption, the continuity equation will be automatically satisfied. The following architecture is used in this example,
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$$
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\begin{equation}
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\psi, p=net\left(t, x, y, \lambda_1, \lambda_2\right)
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\end{equation}
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$$
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### Define Symbols and Geometric Objects
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We define three coordinate symbols `x`, `y` and `t`, three variables $u,v,p$ are defined.
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```python
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x = Symbol('x')
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y = Symbol('y')
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t = Symbol('t')
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geo = sc.Rectangle((1., -2.), (8., 2.))
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u = sp.Function('u')(x, y, t)
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v = sp.Function('v')(x, y, t)
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p = sp.Function('p')(x, y, t)
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time_range = {t: (0, 20)}
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```
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### Define Sampling Methods and Constraints
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This example has only two equation constraints, while the former has six equation constraints. We also use the LAD-PINN model. Then the PDE constrained optimization model is formulated as:
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$$
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\min _{\theta, \lambda} \frac{1}{\# \mathbf{D}_u} \sum_{\left(t_i, x_i, u_i\right) \in \mathbf{D}_u}\left|u_i-u_\theta\left(t_i, x_i ; \lambda\right)\right|+\omega \cdot L_{p d e} .
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$$
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```python
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@sc.datanode(name='NS_domain', loss_fn='L1')
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class NSExternal(sc.SampleDomain):
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def __init__(self):
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points = pd.read_csv('NSexternel_sample.csv')
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self.points = {col: points[col].to_numpy().reshape(-1, 1) for col in points.columns}
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self.constraints = {'u': self.points.pop('u'), 'v': self.points.pop('v'), 'p': self.points.pop('p')}
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def sampling(self, *args, **kwargs):
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return self.points, self.constraints
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@sc.datanode(name='NS_external')
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class NSEq(sc.SampleDomain):
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def sampling(self, *args, **kwargs):
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points = geo.sample_interior(density=2000, param_ranges=time_range)
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constraints = {'continuity': 0, 'momentum_x': 0, 'momentum_y': 0}
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return points, constraints
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```
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### Define Neural Networks and PDEs
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IDRLnet defines a network node to represent the unknown Parameters.
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```python
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net = sc.MLP([3, 20, 20, 20, 20, 20, 20, 20, 20, 3], activation=sc.Activation.tanh)
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net = sc.get_net_node(inputs=('x', 'y', 't'), outputs=('u', 'v', 'p'), name='net', arch=sc.Arch.mlp)
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var_nr = sc.get_net_node(inputs=('x', 'y'), outputs=('nu', 'rho'), arch=sc.Arch.single_var)
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pde = sc.NavierStokesNode(nu='nu', rho='rho', dim=2, time=True, u='u', v='v', p='p')
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```
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### Define A Solver
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Two nodes trained together
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```python
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s = sc.Solver(sample_domains=(NSExternal(), NSEq()),
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netnodes=[net, var_nr],
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pdes=[pde],
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network_dir='network_dir',
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max_iter=10000)
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```
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Finally, the real velocity field and pressure field at t=10s are compared with the predicted results:
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@ -14,6 +14,8 @@ To make full use of IDRLnet. We strongly suggest following the following example
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6. :ref:`Parameterized poisson equation <Parameterized Poisson>`. The example introduces how to train a surrogate with parameters.
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7. :ref:`Variational Minimization <Variational Minimization>`. The example introduces how to solve variational minimization problems.
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8. :ref:`Volterra integral differential equation <Volterra Integral Differential Equation>`. The example introduces the way to solve IDEs.
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9. :ref:`Navier-Stokes equation <Navier-Stokes equations>`. The example introduces how to use the LBFGS optimizer.
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10. :ref:`Deepritz method <Deepritz>`. The example introduces the way to solve PDEs with the Deepritz method.
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@ -28,3 +30,5 @@ To make full use of IDRLnet. We strongly suggest following the following example
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6_parameterized_poisson
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7_minimal_surface
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8_volterra_ide
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9_navier_stokes_equation
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10_deepritz
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|
|
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@ -0,0 +1,75 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import sympy as sp
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import matplotlib.tri as tri
|
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import idrlnet.shortcut as sc
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|
||||
x, y = sp.symbols("x y")
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u = sp.Function("u")(x, y)
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geo = sc.Rectangle((-1, -1), (1., 1.))
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||||
|
||||
|
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@sc.datanode(sigma=1000.0)
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class Boundary(sc.SampleDomain):
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||||
def __init__(self):
|
||||
self.points = geo.sample_boundary(100,)
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self.constraints = {"u": 0.}
|
||||
|
||||
def sampling(self, *args, **kwargs):
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||||
return self.points, self.constraints
|
||||
|
||||
|
||||
@sc.datanode(loss_fn="Identity")
|
||||
class Interior(sc.SampleDomain):
|
||||
def __init__(self):
|
||||
self.points = geo.sample_interior(1000)
|
||||
self.constraints = {"integral_dxdy": 0,}
|
||||
|
||||
def sampling(self, *args, **kwargs):
|
||||
return self.points, self.constraints
|
||||
|
||||
|
||||
def f(x, y):
|
||||
return 2 * sp.pi ** 2 * sp.sin(sp.pi * x) * sp.sin(sp.pi * y)
|
||||
|
||||
|
||||
dx_exp = sc.ExpressionNode(
|
||||
expression=0.5*(u.diff(x) ** 2 + u.diff(y) ** 2) - u * f(x, y), name="dxdy"
|
||||
)
|
||||
net = sc.get_net_node(inputs=("x", "y"), outputs=("u",), name="net", arch=sc.Arch.mlp)
|
||||
|
||||
integral = sc.ICNode("dxdy", dim=2, time=False)
|
||||
|
||||
s = sc.Solver(
|
||||
sample_domains=(Boundary(), Interior()),
|
||||
netnodes=[net],
|
||||
pdes=[
|
||||
dx_exp,
|
||||
integral,
|
||||
],
|
||||
max_iter=10000,
|
||||
)
|
||||
s.solve()
|
||||
coord = s.infer_step({"Interior": ["x", "y", "u"]})
|
||||
num_x = coord["Interior"]["x"].cpu().detach().numpy().ravel()
|
||||
num_y = coord["Interior"]["y"].cpu().detach().numpy().ravel()
|
||||
num_Up = coord["Interior"]["u"].cpu().detach().numpy().ravel()
|
||||
|
||||
# Ground truth
|
||||
num_U = np.sin(np.pi*num_x)*np.sin(np.pi*num_y)
|
||||
|
||||
fig, ax = plt.subplots(1, 3, figsize=(10, 3))
|
||||
triang_total = tri.Triangulation(num_x, num_y)
|
||||
ax[0].tricontourf(triang_total, num_Up, 100, cmap="bwr", vmin=-1, vmax=1)
|
||||
ax[0].axis("off")
|
||||
ax[0].set_title("prediction")
|
||||
ax[1].tricontourf(triang_total, num_U, 100, cmap="bwr", vmin=-1, vmax=1)
|
||||
ax[1].axis("off")
|
||||
ax[1].set_title("ground truth")
|
||||
ax[2].tricontourf(
|
||||
triang_total, np.abs(num_U - num_Up), 100, cmap="bwr", vmin=0, vmax=0.5
|
||||
)
|
||||
ax[2].axis("off")
|
||||
ax[2].set_title("absolute error")
|
||||
|
||||
plt.savefig("deepritz.png", dpi=300, bbox_inches="tight")
|
||||
|
|
@ -63,8 +63,7 @@ solver = sc.Solver(
|
|||
),
|
||||
netnodes=[net],
|
||||
pdes=[pde1, pde2, pde3, pde4],
|
||||
max_iter=2000,
|
||||
)
|
||||
max_iter=2000)
|
||||
solver.solve()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,79 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import sympy as sp
|
||||
import numpy as np
|
||||
import idrlnet.shortcut as sc
|
||||
|
||||
x = sp.symbols('x')
|
||||
Line = sc.Line1D(0, 1)
|
||||
y = sp.Function('y')(x)
|
||||
|
||||
|
||||
@sc.datanode(name='interior')
|
||||
class Interior(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return Line.sample_interior(1000), {'dddd_y': 0}
|
||||
|
||||
|
||||
@sc.datanode(name='left_boundary1')
|
||||
class LeftBoundary1(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return Line.sample_boundary(100, sieve=(sp.Eq(x, 0))), {'y': 0}
|
||||
|
||||
|
||||
@sc.datanode(name='left_boundary2')
|
||||
class LeftBoundary2(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return Line.sample_boundary(100, sieve=(sp.Eq(x, 0))), {'d_y': 0}
|
||||
|
||||
|
||||
@sc.datanode(name='right_boundary1')
|
||||
class RightBoundary1(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return Line.sample_boundary(100, sieve=(sp.Eq(x, 1))), {'dd_y': 0}
|
||||
|
||||
|
||||
@sc.datanode(name='right_boundary2')
|
||||
class RightBoundary2(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return Line.sample_boundary(100, sieve=(sp.Eq(x, 1))), {'ddd_y': 0}
|
||||
|
||||
|
||||
@sc.datanode(name='infer')
|
||||
class Infer(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
return {'x': np.linspace(0, 1, 1000).reshape(-1, 1)}, {}
|
||||
|
||||
|
||||
net = sc.get_net_node(inputs=('x',), outputs=('y',), name='net', arch=sc.Arch.mlp)
|
||||
|
||||
pde1 = sc.ExpressionNode(name='dddd_y', expression=y.diff(x).diff(x).diff(x).diff(x) + 1)
|
||||
pde2 = sc.ExpressionNode(name='d_y', expression=y.diff(x))
|
||||
pde3 = sc.ExpressionNode(name='dd_y', expression=y.diff(x).diff(x))
|
||||
pde4 = sc.ExpressionNode(name='ddd_y', expression=y.diff(x).diff(x).diff(x))
|
||||
|
||||
solver = sc.Solver(
|
||||
sample_domains=(Interior(), LeftBoundary1(), LeftBoundary2(), RightBoundary1(), RightBoundary2()),
|
||||
netnodes=[net],
|
||||
pdes=[pde1, pde2, pde3, pde4],
|
||||
max_iter=200,
|
||||
opt_config=dict(optimizer='LBFGS', lr=1))
|
||||
solver.solve()
|
||||
|
||||
|
||||
# inference
|
||||
def exact(x):
|
||||
return -(x ** 4) / 24 + x ** 3 / 6 - x ** 2 / 4
|
||||
|
||||
|
||||
solver.sample_domains = (Infer(),)
|
||||
points = solver.infer_step({'infer': ['x', 'y']})
|
||||
xs = points['infer']['x'].detach().cpu().numpy().ravel()
|
||||
y_pred = points['infer']['y'].detach().cpu().numpy().ravel()
|
||||
plt.plot(xs, y_pred, label='Pred')
|
||||
y_exact = exact(xs)
|
||||
plt.plot(xs, y_exact, label='Exact', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('x')
|
||||
plt.ylabel('w')
|
||||
plt.savefig('Euler_beam_LBFGS.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,194 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import sympy as sp
|
||||
import numpy as np
|
||||
import idrlnet.shortcut as sc
|
||||
from sympy import Symbol, sin
|
||||
import pandas as pd
|
||||
import torch
|
||||
import matplotlib.tri as tri
|
||||
|
||||
x = Symbol('x')
|
||||
y = Symbol('y')
|
||||
rec = sc.Rectangle((0., 0.), (1.1, 0.41))
|
||||
cir = sc.Circle((0.2, 0.2), 0.05)
|
||||
geo = rec - cir
|
||||
u = sp.Function('u')(x, y)
|
||||
v = sp.Function('v')(x, y)
|
||||
p = sp.Function('p')(x, y)
|
||||
s11 = sp.Function('s11')(x, y)
|
||||
s22 = sp.Function('s22')(x, y)
|
||||
s12 = sp.Function('s12')(x, y)
|
||||
nu=0.02
|
||||
rho=1
|
||||
|
||||
@sc.datanode
|
||||
class Inlet(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
points = rec.sample_boundary(1000, sieve=(sp.Eq(x, 0.)))
|
||||
constraints = sc.Variables({'u': 4 * (0.41 - y) * y / (0.41 * 0.41)})
|
||||
return points, constraints
|
||||
|
||||
@sc.datanode
|
||||
class Outlet(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
points = geo.sample_boundary(1000, sieve=(sp.Eq(x, 1.1)))
|
||||
constraints = sc.Variables({'p': 0.})
|
||||
return points, constraints
|
||||
|
||||
@sc.datanode
|
||||
class Wall(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
points = geo.sample_boundary(1000, sieve=((x > 0.) & (x < 1.1)))
|
||||
#print("points3", points)
|
||||
constraints = sc.Variables({'u': 0., 'v': 0.})
|
||||
return points, constraints
|
||||
|
||||
@sc.datanode(name='NS_external')
|
||||
class Interior_domain(sc.SampleDomain):
|
||||
def __init__(self):
|
||||
self.density = 2000
|
||||
|
||||
def sampling(self, *args, **kwargs):
|
||||
points = geo.sample_interior(2000)
|
||||
constraints = {'f_s11': 0., 'f_s22': 0., 'f_s12': 0., 'f_u': 0., 'f_v': 0., 'f_p': 0.}
|
||||
return points, constraints
|
||||
|
||||
@sc.datanode(name='NS_domain', loss_fn='L1')
|
||||
class NSExternal(sc.SampleDomain):
|
||||
def __init__(self):
|
||||
points = pd.read_csv('NSexternel_sample.csv')
|
||||
self.points = {col: points[col].to_numpy().reshape(-1, 1) for col in points.columns}
|
||||
self.constraints = {'u': self.points.pop('u'), 'v': self.points.pop('v'), 'p': self.points.pop('p')}
|
||||
|
||||
def sampling(self, *args, **kwargs):
|
||||
return self.points, self.constraints
|
||||
|
||||
net = sc.MLP([2, 40, 40, 40, 40, 40, 40, 40, 40, 6], activation=sc.Activation.tanh)
|
||||
net = sc.get_net_node(inputs=('x', 'y'), outputs=('u', 'v', 'p', 's11', 's22', 's12'), name='net', arch=sc.Arch.mlp)
|
||||
pde1 = sc.ExpressionNode(name='f_s11', expression=-p + 2 * nu * u.diff(x) - s11)
|
||||
pde2 = sc.ExpressionNode(name='f_s22', expression=-p + 2 * nu * v.diff(y) - s22)
|
||||
pde3 = sc.ExpressionNode(name='f_s12', expression=nu * (u.diff(y) + v.diff(x)) - s12)
|
||||
pde4 = sc.ExpressionNode(name='f_u', expression=u * u.diff(x) + v * u.diff(y) - nu * (s11.diff(x) + s12.diff(y)))
|
||||
pde5 = sc.ExpressionNode(name='f_v', expression=u * v.diff(x) + v * v.diff(y) - nu * (s12.diff(x) + s22.diff(y)))
|
||||
pde6 = sc.ExpressionNode(name='f_p', expression=p + (s11 + s22) / 2)
|
||||
s = sc.Solver(sample_domains=(Inlet(), Outlet(), Wall(), Interior_domain(), NSExternal()),
|
||||
netnodes=[net],
|
||||
init_network_dirs=['network_dir_adam'],
|
||||
pdes=[pde1, pde2, pde3, pde4, pde5, pde6],
|
||||
max_iter=300,
|
||||
opt_config = dict(optimizer='LBFGS', lr=1)
|
||||
)
|
||||
#opt_config = dict(optimizer='LBFGS', lr=1)
|
||||
#init_network_dirs=['network_dir_lbfgs'],
|
||||
s.solve()
|
||||
|
||||
points1 = pd.read_csv('NSexternel_test.csv')
|
||||
points1 = {col: points1[col].to_numpy().reshape(-1, 1) for col in points1.columns}
|
||||
x_test = torch.tensor(points1['x_test'].astype(np.float32))
|
||||
y_test = torch.tensor(points1['y_test'].astype(np.float32))
|
||||
u_test = torch.tensor(points1['u_test'].astype(np.float32))
|
||||
v_test = torch.tensor(points1['v_test'].astype(np.float32))
|
||||
p_test = torch.tensor(points1['p_test'].astype(np.float32))
|
||||
|
||||
U = s.netnodes[0].net(torch.cat([x_test, y_test], dim=1))
|
||||
|
||||
num_x = x_test.cpu().detach().numpy().ravel()
|
||||
num_y = y_test.cpu().detach().numpy().ravel()
|
||||
num_u = u_test.cpu().detach().numpy().ravel()
|
||||
num_v = v_test.cpu().detach().numpy().ravel()
|
||||
num_p = p_test.cpu().detach().numpy().ravel()
|
||||
|
||||
num_up = U[:, 0:1].cpu().detach().numpy().ravel()
|
||||
num_vp = U[:, 1:2].cpu().detach().numpy().ravel()
|
||||
num_pp = U[:, 2:3].cpu().detach().numpy().ravel()
|
||||
|
||||
|
||||
triang_total = tri.Triangulation(num_x, num_y)
|
||||
|
||||
font2 = {'family': 'Times New Roman',
|
||||
'weight': 'normal',
|
||||
'size': 15,
|
||||
}
|
||||
|
||||
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_u, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $u$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_up, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $u$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_u - num_up, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_u_Adam.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_v, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $v$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_vp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $v$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_v - num_vp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_v_Adam.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_p, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $p$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_pp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $p$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_p - num_pp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_p_Adam.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import sympy as sp
|
||||
import numpy as np
|
||||
import idrlnet.shortcut as sc
|
||||
from sympy import Symbol, sin
|
||||
import pandas as pd
|
||||
import torch
|
||||
import matplotlib.tri as tri
|
||||
|
||||
x = Symbol('x')
|
||||
y = Symbol('y')
|
||||
t = Symbol('t')
|
||||
geo = sc.Rectangle((1., -2.), (8., 2.))
|
||||
u = sp.Function('u')(x, y, t)
|
||||
v = sp.Function('v')(x, y, t)
|
||||
p = sp.Function('p')(x, y, t)
|
||||
time_range = {t: (0, 20)}
|
||||
nu=0.01
|
||||
rho=1
|
||||
|
||||
@sc.datanode(name='NS_domain', loss_fn='L1')
|
||||
class NSExternal(sc.SampleDomain):
|
||||
def __init__(self):
|
||||
points = pd.read_csv('NSexternel_sample.csv')
|
||||
self.points = {col: points[col].to_numpy().reshape(-1, 1) for col in points.columns}
|
||||
self.constraints = {'u': self.points.pop('u'), 'v': self.points.pop('v'), 'p': self.points.pop('p')}
|
||||
|
||||
def sampling(self, *args, **kwargs):
|
||||
return self.points, self.constraints
|
||||
|
||||
@sc.datanode(name='NS_external')
|
||||
class NSEq(sc.SampleDomain):
|
||||
def sampling(self, *args, **kwargs):
|
||||
points = geo.sample_interior(density=2000, param_ranges=time_range)
|
||||
constraints = {'continuity': 0, 'momentum_x': 0, 'momentum_y': 0}
|
||||
return points, constraints
|
||||
|
||||
net = sc.MLP([3, 20, 20, 20, 20, 20, 20, 20, 20, 3], activation=sc.Activation.tanh)
|
||||
net = sc.get_net_node(inputs=('x', 'y', 't'), outputs=('u', 'v', 'p'), name='net', arch=sc.Arch.mlp)
|
||||
#var_nr = sc.get_net_node(inputs=('x', 'y'), outputs=('nu', 'rho'), arch=sc.Arch.single_var)
|
||||
#pde = sc.NavierStokesNode(nu='nu', rho='rho', dim=2, time=True, u='u', v='v', p='p')
|
||||
pde = sc.NavierStokesNode(nu=0.01, rho=1.0, dim=2, time=True)
|
||||
s = sc.Solver(sample_domains=(NSExternal(), NSEq()),
|
||||
netnodes=[net],
|
||||
init_network_dirs=['network_dir_adam'],
|
||||
pdes=[pde],
|
||||
max_iter=100,
|
||||
opt_config=dict(optimizer='LBFGS', lr=1)
|
||||
)
|
||||
#opt_config=dict(optimizer='LBFGS', lr=1)
|
||||
# s = sc.Solver(sample_domains=(NSExternal(), NSEq()),
|
||||
# netnodes=[net, var_nr],
|
||||
# pdes=[pde],
|
||||
# network_dir='network_dir',
|
||||
# max_iter=10)
|
||||
s.solve()
|
||||
|
||||
|
||||
coord = s.infer_step(domain_attr={'NS_domain': ['x', 'y', 'u', 'v', 'p']})
|
||||
num_xd = coord['NS_domain']['x'].cpu().detach().numpy().ravel()
|
||||
num_yd = coord['NS_domain']['y'].cpu().detach().numpy().ravel()
|
||||
num_ud = coord['NS_domain']['u'].cpu().detach().numpy().ravel()
|
||||
num_vd = coord['NS_domain']['v'].cpu().detach().numpy().ravel()
|
||||
num_pd = coord['NS_domain']['p'].cpu().detach().numpy().ravel()
|
||||
|
||||
# print("true paratmeter rho: {:.4f}".format(rho))
|
||||
# predict_rho = var_nr.evaluate(torch.Tensor([[1.0]])).item()
|
||||
# print("predicted parameter rho: {:.4f}".format(predict_rho))
|
||||
|
||||
|
||||
points1 = pd.read_csv('NSexternel_test.csv')
|
||||
points1 = {col: points1[col].to_numpy().reshape(-1, 1) for col in points1.columns}
|
||||
x_test = torch.tensor(points1['x_test'].astype(np.float32))
|
||||
y_test = torch.tensor(points1['y_test'].astype(np.float32))
|
||||
t_test = torch.tensor(points1['t_test'].astype(np.float32))
|
||||
u_test = torch.tensor(points1['u_test'].astype(np.float32))
|
||||
v_test = torch.tensor(points1['v_test'].astype(np.float32))
|
||||
p_test = torch.tensor(points1['p_test'].astype(np.float32))
|
||||
|
||||
U = s.netnodes[0].net(torch.cat([x_test, y_test, t_test], dim=1))
|
||||
|
||||
num_x = x_test.cpu().detach().numpy().ravel()
|
||||
num_y = y_test.cpu().detach().numpy().ravel()
|
||||
num_u = u_test.cpu().detach().numpy().ravel()
|
||||
num_v = v_test.cpu().detach().numpy().ravel()
|
||||
num_p = p_test.cpu().detach().numpy().ravel()
|
||||
|
||||
num_up = U[:, 0:1].cpu().detach().numpy().ravel()
|
||||
num_vp = U[:, 1:2].cpu().detach().numpy().ravel()
|
||||
num_pp = U[:, 2:3].cpu().detach().numpy().ravel()
|
||||
|
||||
|
||||
triang_total = tri.Triangulation(num_x, num_y)
|
||||
|
||||
font2 = {'family': 'Times New Roman',
|
||||
'weight': 'normal',
|
||||
'size': 15,
|
||||
}
|
||||
|
||||
# fig = plt.figure(figsize=(6, 6))
|
||||
# ax = fig.add_subplot(111)
|
||||
# ax.scatter(num_xi, num_yi, c='b', s=1, label='Domain')
|
||||
# ax.set_xlabel('$x$', font2)
|
||||
# ax.set_ylabel('$y$', font2)
|
||||
# ax.set_title('collocation points', fontsize=18)
|
||||
# plt.savefig('points.png', dpi=300, bbox_inches='tight')
|
||||
# plt.show()
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_u, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $u$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_up, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $u$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_u - num_up, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_u_c.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_v, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $v$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_v, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $v$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_v - num_vp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_v_c.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
fig = plt.figure(figsize=(20, 4))
|
||||
ax1 = fig.add_subplot(131)
|
||||
tcf = ax1.tricontourf(triang_total, num_p, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax1.set_xlabel('$x$', font2)
|
||||
ax1.set_ylabel('$y$', font2)
|
||||
ax1.set_title('Exact $p$', fontsize=18)
|
||||
|
||||
ax2 = fig.add_subplot(132)
|
||||
tcf = ax2.tricontourf(triang_total, num_pp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax2.set_xlabel('$x$', font2)
|
||||
ax2.set_ylabel('$y$', font2)
|
||||
ax2.set_title('Predicted $p$', fontsize=18)
|
||||
|
||||
ax3 = fig.add_subplot(133)
|
||||
tcf = ax3.tricontourf(triang_total, num_p - num_pp, 100, cmap='jet')
|
||||
tc_bar = plt.colorbar(tcf)
|
||||
tc_bar.ax.tick_params(labelsize=12)
|
||||
ax3.set_xlabel('$x$', font2)
|
||||
ax3.set_ylabel('$y$', font2)
|
||||
ax3.set_title('Point-wise Error', fontsize=18)
|
||||
plt.savefig('test_NS_p_c.png', dpi=300, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
|
@ -312,6 +312,20 @@ class Solver(Notifier, Optimizable):
|
|||
"""
|
||||
self.notify(self, message={Signal.TRAIN_PIPE_START: "defaults"})
|
||||
for opt in self.optimizers:
|
||||
# print('Running optimization with %s'%(self.optimizer_config['optimizer']))
|
||||
if self.optimizer_config['optimizer'] == 'LBFGS':
|
||||
def closure():
|
||||
opt.zero_grad()
|
||||
samples = self.sample_variables_from_domains()
|
||||
in_var, true_out, lambda_out = self.generate_in_out_dict(samples)
|
||||
pred_out_sample = self.forward_through_all_graph(in_var, self.outvar_dict_index)
|
||||
loss = self.compute_loss(in_var, pred_out_sample, true_out, lambda_out)
|
||||
self.notify(self, message={Signal.BEFORE_BACKWARD: 'defaults'})
|
||||
loss.backward()
|
||||
return loss
|
||||
opt.step(closure)
|
||||
|
||||
else:
|
||||
opt.zero_grad()
|
||||
samples = self.sample_variables_from_domains()
|
||||
in_var, true_out, lambda_out = self.generate_in_out_dict(samples)
|
||||
|
|
@ -320,10 +334,15 @@ class Solver(Notifier, Optimizable):
|
|||
loss = self.compute_loss(in_var, pred_out_sample, true_out, lambda_out)
|
||||
except RuntimeError:
|
||||
raise
|
||||
self.notify(self, message={Signal.BEFORE_BACKWARD: "defaults"})
|
||||
self.notify(self, message={Signal.BEFORE_BACKWARD: 'defaults'})
|
||||
loss.backward()
|
||||
for opt in self.optimizers:
|
||||
opt.step()
|
||||
|
||||
samples = self.sample_variables_from_domains()
|
||||
in_var, true_out, lambda_out = self.generate_in_out_dict(samples)
|
||||
pred_out_sample = self.forward_through_all_graph(in_var, self.outvar_dict_index)
|
||||
loss = self.compute_loss(in_var, pred_out_sample, true_out, lambda_out)
|
||||
|
||||
self.global_step += 1
|
||||
|
||||
for scheduler in self.schedulers:
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ class Loss(enum.Enum):
|
|||
|
||||
L1 = "L1"
|
||||
square = "square"
|
||||
Identity = "Identity"
|
||||
|
||||
|
||||
class LossFunction:
|
||||
|
|
@ -32,6 +33,8 @@ class LossFunction:
|
|||
return LossFunction.weighted_L1_loss(variables, name=name)
|
||||
elif loss_function == Loss.square.name or loss_function == Loss.square:
|
||||
return LossFunction.weighted_square_loss(variables, name=name)
|
||||
elif loss_function == Loss.Identity.name or loss_function == Loss.Identity:
|
||||
return LossFunction.weighted_identity_loss(variables, name=name)
|
||||
raise NotImplementedError(f"loss function {loss_function} is not defined!")
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -62,6 +65,20 @@ class LossFunction:
|
|||
loss += torch.sum((val ** 2) * variables["area"])
|
||||
return Variables({name: loss})
|
||||
|
||||
@staticmethod
|
||||
def weighted_identity_loss(variables: "Variables", name: str) -> "Variables":
|
||||
loss = 0.0
|
||||
for key, val in variables.items():
|
||||
if key.startswith("lambda_") or key == "area":
|
||||
continue
|
||||
elif "lambda_" + key in variables.keys():
|
||||
loss += torch.sum(
|
||||
val * variables["lambda_" + key] * variables["area"]
|
||||
)
|
||||
else:
|
||||
loss += torch.sum(val * variables["area"])
|
||||
return Variables({name: loss})
|
||||
|
||||
|
||||
class Variables(dict):
|
||||
def __sub__(self, other: "Variables") -> "Variables":
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
transforms3d
|
||||
typing
|
||||
numpy
|
||||
keras
|
||||
h5py
|
||||
pandas
|
||||
|
|
@ -11,12 +10,13 @@ sphinx
|
|||
matplotlib
|
||||
myst_parser
|
||||
sphinx_markdown_parser
|
||||
numpy==1.21.0
|
||||
sphinx_rtd_theme==0.5.2
|
||||
tensorboard==2.4.1
|
||||
sympy==1.5.1
|
||||
pyevtk==1.1.1
|
||||
flask==1.1.2
|
||||
requests==2.25.0
|
||||
torch>=1.7.1
|
||||
torch==1.7.1
|
||||
networkx==2.5.1
|
||||
protobuf~=3.20
|
||||
2
setup.py
2
setup.py
|
|
@ -21,7 +21,7 @@ def load_requirements(path_dir=here, comment_char="#"):
|
|||
|
||||
setuptools.setup(
|
||||
name="idrlnet", # Replace with your own username
|
||||
version="0.1.0",
|
||||
version="2.0.0-rc3",
|
||||
author="Intelligent Design & Robust Learning lab",
|
||||
author_email="weipeng@deepinfar.cn",
|
||||
description="IDRLnet",
|
||||
|
|
|
|||
Loading…
Reference in New Issue