model_inverse.py

import torch
import numpy as np
import matplotlib.pyplot as plt
from Dataset import Dataset

# DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
DEVICE = torch.device('cpu')

# Set global plotting parameters
plt.rcParams.update({'font.size': 14,
                     'figure.figsize': (10, 8),
                     'lines.linewidth':  2,
                     'lines.markersize': 6,
                     'axes.grid': True,
                     'axes.labelsize': 16,
                     'legend.fontsize': 14,
                     'xtick.labelsize': 14,
                     'ytick.labelsize': 14,
                     'figure.autolayout': True
                     })

def set_seed(seed=42):
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)    

class NeuralNetwork(torch.nn.Module):
    def __init__(self, layer_sizes, dropout_rate=0.0, activation=torch.nn.ReLU):
        super(NeuralNetwork, self).__init__()

        if dropout_rate > 0:
            self.dropout_layer = torch.nn.Dropout(dropout_rate)

        self.layer_sizes = layer_sizes
        self.layers = torch.nn.ModuleList()
        for i in range(len(layer_sizes) - 2):
            self.layers.append(torch.nn.Linear(layer_sizes[i], layer_sizes[i + 1]))
            self.layers.append(activation())
        self.layers.append(torch.nn.Linear(layer_sizes[-2], layer_sizes[-1]))

        # self.sequential = torch.nn.Sequential(*self.layers)

        self.init_weights()

    def init_weights(self):
        for layer in self.layers:
            if isinstance(layer, torch.nn.Linear):
                torch.nn.init.xavier_normal_(layer.weight)
                layer.bias.data.fill_(0.0)

    def forward(self, x, train=True):
        for layer in self.layers:
            x = layer(x)
            if train and hasattr(self, 'dropout_layer'):
                x = self.dropout_layer(x)

        return x
    
    def predict(self, x, train=False):
        self.eval()
        with torch.no_grad():
            return self.forward(x, train)

def train_neural_network(model, inputs, outputs, optimizer, epochs=1000, lr_scheduler=None):
    model.train()
    for epoch in range(epochs):
        optimizer.zero_grad()
        predictions = model(inputs)
        loss = torch.mean(torch.square(predictions - outputs))
        loss.backward()
        optimizer.step()

        if lr_scheduler:
            lr_scheduler.step()

        if epoch % 100 == 0:
            print(f'Epoch {epoch}, Loss: {loss.item()}, Learning Rate: {optimizer.param_groups[0]["lr"]}')


def load_model(model_path):
    checkpoint = torch.load(model_path, map_location=DEVICE)
    model_config = checkpoint['model_config']
    model = NeuralNetwork(model_config['layer_sizes'], dropout_rate=model_config['dropout_rate'])
    model.load_state_dict(checkpoint['model_state_dict'])
    print(f"Model loaded from {model_path}")

    model.to(DEVICE)
    model.eval()
    return model

def inverse_design(gate_loc, matrix, fiber, fiber_vf, y_target, n_restarts=10, epochs=100, use_lbfgs=False, feasibility_samples=0):
    model = load_model('./model_checkpoint.pth')

    data = Dataset()
    mat_type = data.material_map.get(matrix, 0.0)
    fiber_type = data.fiber_map.get(fiber, 0.0)
    
    y_target_norm = data.normalize_output(y_target) # (A1, B1, C1, Stress)
    y_target_tensor = torch.tensor(y_target, dtype=torch.float32)
    input_mean = torch.tensor(data.input_mean)
    input_std = torch.tensor(data.input_std)
    output_mean = torch.tensor(data.output_mean)
    output_std = torch.tensor(data.output_std)

    
    weights = torch.tensor([1.0, 1.0, 1.0], dtype=torch.float32)
    bounds = torch.tensor([[1., 100.], [1., 10.], [1., 100.], [1., 100.]], dtype=torch.float32)
    best = {"loss": float('inf'), "input": None, "output": None}

    for restart in range(n_restarts):
        z = torch.randn(4, requires_grad=True)

        if use_lbfgs:
            optimizer = torch.optim.LBFGS([z], lr=0.1, max_iter=epochs, line_search_fn="strong_wolfe")
            steps = 1
        else:
            optimizer = torch.optim.Adam([z], lr=0.001)
            steps = epochs
        
        for step in range(steps):
            def closure():
                var =  bounds[:, 0] + (bounds[:, 1] - bounds[:, 0]) * torch.sigmoid(z)
                optimizer.zero_grad()
                input_raw = torch.cat([torch.tensor([gate_loc, mat_type, fiber_type, fiber_vf]), var]).unsqueeze(0)
                input_norm = (input_raw - input_mean) / input_std
                output_pred = model(input_norm, train=False)
                output_pred = (output_pred * output_std) + output_mean
                loss = torch.sum(weights * (output_pred - y_target_tensor) ** 2)
                loss.backward()
                return loss

            if use_lbfgs:
                loss = optimizer.step(closure)
            else:
                loss = closure()
                optimizer.step()

            if (step + 1) % 200 == 0:
                print(f'Restart {restart + 1}, Step {step + 1}, Loss: {loss.item():.6f}, grad: {z.grad.norm().item():.6f}')
            
        with torch.no_grad():
            var =  bounds[:, 0] + (bounds[:, 1] - bounds[:, 0]) * torch.sigmoid(z)
            input_raw = torch.cat([torch.tensor([gate_loc, mat_type, fiber_type, fiber_vf]), var])
            input_norm = (input_raw - input_mean) / input_std
            output_pred = model.predict(input_norm)
            output_pred = data.denormalize_output(output_pred.numpy())
            final_loss = np.sum(weights.numpy() * (output_pred - y_target) ** 2).item()
            if final_loss < best["loss"]:
                best["loss"] = final_loss
                best["input"] = var.detach().cpu().numpy()
                best["output"] = output_pred

    return best


if __name__ == "__main__":
    # set_seed(5324)
    # train the inverse model over springback data
    # inverse_model()

    # perform inverse design
    import time
    start_time = time.time()
    best = inverse_design(gate_loc=1, matrix='PA6', fiber='CF', fiber_vf=0.4, y_target=np.array([0.45, 9.03, 1.87]), n_restarts=5, epochs=100, use_lbfgs=True)
    end_time = time.time()
    time_elapsed = (end_time - start_time)
    print(f"Inverse design completed in {time_elapsed:.2f} seconds.")
    print("Best Input:", best["input"])
    print("Best Output:", best["output"])