02_PINN_简化.py

import sys
from sklearn.model_selection import train_test_split
sys.path.append(".")
import numpy as np
import torch
from torch.autograd import grad
from network import DNN
from scipy.io import loadmat
import pandas as pd
import torch
import torch.nn as nn
import numpy as np
import os


device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
torch.manual_seed(1234)
np.random.seed(1234)

N_u = 2000

data = loadmat(r"D:\02_github\03_PINNs\PINNs-torch\Burgers\burgers_shock.mat")

x = data["x"]
t = data["t"]
u = data["usol"].T

ub = np.array([x.max(), t.max()])
lb = np.array([x.min(), t.min()])

x_, t_ = np.meshgrid(x, t)
x_ = x_.reshape(-1, 1)
t_ = t_.reshape(-1, 1)
u_ = u.reshape(-1, 1)

rand_idx = np.random.choice(len(u_), N_u, replace=False)

x = torch.tensor(x_[rand_idx], dtype=torch.float32).to(device)
t = torch.tensor(t_[rand_idx], dtype=torch.float32).to(device)
xt = torch.cat((x, t), dim=1)
u = torch.tensor(u_[rand_idx], dtype=torch.float32).to(device)

noise = 0.01
noisy_u = u_ + noise * np.std(u_) * np.random.randn(*u_.shape)
noisy_u = torch.tensor(noisy_u[rand_idx], dtype=torch.float32).to(device)

class PINN:
    def __init__(self, u):
        self.u = u
        self.lambda_1 = torch.tensor([0.0], requires_grad=True).to(device)
        self.lambda_2 = torch.tensor([-6.0], requires_grad=True).to(device)
        self.lambda_1 = torch.nn.Parameter(self.lambda_1)
        self.lambda_2 = torch.nn.Parameter(self.lambda_2)
        self.net = DNN(dim_in=2, dim_out=1, n_layer=7, n_node=20, ub=ub, lb=lb,).to(
            device
        )
        self.net.register_parameter("lambda_1", self.lambda_1)
        self.net.register_parameter("lambda_2", self.lambda_2)

        self.optimizer = torch.optim.LBFGS(
            self.net.parameters(),
            lr=1.0,
            max_iter=50000,
            max_eval=50000,
            history_size=50,
            tolerance_grad=1e-5,
            tolerance_change=1.0 * np.finfo(float).eps,
            line_search_fn="strong_wolfe",
        )
        self.iter = 0

    def f(self, xt):
        xt = xt.clone()
        xt.requires_grad = True

        u = self.net(xt)

        f = grad(u.sum(), xt, create_graph=True)[0]
        f = torch.clamp(f, 0, 365)
        return f

    def closure(self):
        self.optimizer.zero_grad()

        u_pred = self.net(xt)
        f_pred = self.f(xt)

        mse_u = torch.mean(torch.square(u_pred - self.u))
        mse_f = torch.mean(torch.square(f_pred))

        loss = mse_u + mse_f
        loss.backward()

        self.iter += 1
        print(
            f"\r{self.iter} loss : {loss.item():.3e} l1 : {self.lambda_1.item():.5f}, l2 : {torch.exp(self.lambda_2).item():.5f}",
            end="",
        )
        if self.iter % 500 == 0:
            print("")
        return loss

def calcError(pinn):
    u_pred = pinn.net(torch.hstack((x, t)))
    u_pred = u_pred.detach().cpu().numpy()
    u_ = u.detach().cpu().numpy()
    error_u = np.linalg.norm(u_ - u_pred, 2) / np.linalg.norm(u_, 2)
    lambda1 = pinn.lambda_1.detach().cpu().item()
    lambda2 = np.exp(pinn.lambda_2.detach().cpu().item())
    error_lambda1 = np.abs(lambda1 - 1.0) * 100
    error_lambda2 = np.abs(lambda2 - 0.01 / np.pi) * 100
    print(
        f"\nError u  : {error_u:.5e}",
        f"\nError l1 : {error_lambda1:.5f}%",
        f"\nError l2 : {error_lambda2:.5f}%",
    )
    return (error_u, error_lambda1, error_lambda2)

if __name__ == "__main__":
    pinn = PINN(u)
    pinn.optimizer.step(pinn.closure)
    torch.save(pinn.net.state_dict(), r"D:\02_github\03_PINNs\PINNs-torch\Burgers\Identification\weight_clean3.pt")
    pinn.net.load_state_dict(torch.load(r"D:\02_github\03_PINNs\PINNs-torch\Burgers\Identification\weight_clean3.pt"))
    calcError(pinn)