Update TO_Problem.py

Linear_TO/TO_Problem.py

@@ -1,11 +1,15 @@
 # -*- coding: utf-8 -*-
-"""
-Created on Tue Aug 11 14:20:11 2020
-
-@author: zzy
-
-定义设计域
-"""
+'''
+@ Copyright (c) 2022 by Zeyu Zhang, All Rights Reserved. 
+@ Author       : Zeyu Zhang
+@ Email        : zhangzeyu_work@outlook.com
+@ Date         : 2022-10-25 09:30:27
+@ LastEditTime : 2022-10-25 09:33:20
+@ FilePath     : /ZZY_CODE/Env_JAX/IDRL/Linear_TO/TO_Problem.py
+@ 
+@ Description  : 
+@ Reference    : 
+'''
 
 import jax.ops as jops
 import numpy as np
@@ -81,49 +85,5 @@ def cantilever_single_NN(TopOpt):
     return model_kwargs
 
 
-def cantilever_single_big():
-    L = 1.6
-    W = 0.8
-    nelx = 160
-    nely = 80
-    design_condition = 2
-    Total_dof = 2*(nelx+1)*(nely+1)
-    ele_length = L/nelx
-    ele_width = W/nely
-    ele_thickness = 1.0
-    volfrac = 0.5
-    dofs = np.arange(Total_dof)
-    loaddof = Total_dof-1
-    # fext = jnp.zeros(Total_dof)
-    # fext = jops.index_update(fext, jops.index[loaddof], -1.)
-    fext = np.zeros(Total_dof)
-    fext[loaddof] = -1
-    fixeddofs = np.array(dofs[0:2*(nely+1):1])
-    freedofs = np.setdiff1d(dofs, fixeddofs)
-    filter_width = 3.0
-    design_map = np.ones([nely, nelx], dtype=np.int64)
-    return ToProblem(L, W, nelx, nely, design_condition, ele_length, ele_width, ele_thickness, volfrac, fixeddofs, freedofs, fext, filter_width, design_map)
 
 
-def cantilever_single_big_NN(TopOpt):
-    dense_input_channel = 128
-    dense_init_scale = 1.0
-    conv_input_0 = 32
-    up_sampling_scale = (1, 2, 2, 2, 2)
-    total_resize = int(np.prod(up_sampling_scale))
-    h = TopOpt.nely // total_resize
-    w = TopOpt.nelx // total_resize
-    dense_output_channel = h * w * conv_input_0
-    dense_scale_init = dense_init_scale * \
-        jnp.sqrt(jnp.maximum(dense_output_channel / dense_input_channel, 1))
-    model_kwargs = {
-        'h': h,
-        'w': w,
-        'dense_scale_init': dense_scale_init,
-        'dense_output_channel': dense_output_channel,
-        'dense_input_channel': dense_input_channel,
-        'conv_input_0': conv_input_0,
-        'up_sampling_scale': up_sampling_scale,
-    }
-    return model_kwargs
-