这当然不是说这样做没有应用用途,但作为标题吸引读者然后带来失望对领域发展就不太好了。 这一节了解了单纯数据驱动的局限性,导出了PINN,下一节就从零开始,在Pytorch加持下用PINN解一个稍微不太常见的方程。 3. 基于PyTorch的PINN求解 前面讲了一堆,这节就用前面提到的PINN方法来求解一个具体问题。考虑下面这样...
elasticity_plate.py fractional_Poisson_1d.py fractional_Poisson_2d.py fractional_Poisson_3d.py fractional_diffusion_1d.py heat.py heat_resample.py ide.py ode_2nd.py ode_system.py wave_1d.py pinn_inverse Makefile sample_to_test.py .codacy.yml ...
elasticity_plate.py fractional_Poisson_1d.py fractional_Poisson_2d.py fractional_Poisson_3d.py fractional_diffusion_1d.py heat.py heat_resample.py ide.py ode_2nd.py ode_system.py wave_1d.py pinn_inverse Makefile sample_to_test.py .codacy.yml ...
elasticity_plate.py fractional_Poisson_1d.py fractional_Poisson_2d.py fractional_Poisson_3d.py fractional_diffusion_1d.py heat.py heat_resample.py ide.py ode_2nd.py ode_system.py wave_1d.py pinn_inverse Makefile sample_to_test.py .codacy.yml ...
//arxiv.org/abs/2111.02801. """ import deepxde as dde import numpy as np from scipy.io import loadmat # Import tf if using backend tensorflow.compat.v1 or tensorflow from deepxde.backend import tf # Import torch if using backend pytorch # import torch # Import paddle if using backend ...
"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch, paddle""" import deepxde as dde import numpy as np def gen_testdata(): data = np.load("../dataset/Burgers.npz") t, x, exact = data["t"], data["x"], data["usol"].T xx, tt = np.meshgrid(x, t) X = np....