""" RDE Neural Surrogate v1.0 Predicts full 2D spatial fields from operating conditions [phi, p0, T0, Dcj]. """ import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader import numpy as np import pandas as pd from sklearn.model_selection import train_test_split import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt # ============================================================================= # DATASET: Condition -> Spatial Field Mapping # ============================================================================= class RDESurrogateDataset(Dataset): def __init__(self, npz_path, csv_path='RDE_hybrid_dataset_v9.csv'): data = np.load(npz_path) self.fields = data['fields'] self.is_synthetic = data.get('is_synthetic', np.zeros(72, dtype=bool)) df = pd.read_csv(csv_path) df = df.groupby('case').first().reset_index() self.conditions = np.zeros((72, 4), dtype=np.float32) for i in range(12): case_name = f'RDE_{i+1:02d}' case_df = df[df['case'] == case_name] if len(case_df) > 0: self.conditions[i] = [ float(case_df['phi'].iloc[0]), float(case_df['p0_pa'].iloc[0]), float(case_df['T0_k'].iloc[0]), float(case_df['cantera_cj_speed_ms'].iloc[0]) ] try: synth_data = np.load('output/synthetic_dataset.npz') self.conditions[12:] = synth_data['conditions'] except: print("Warning: Using interpolated conditions for synthetic cases") for i in range(12, 72): idx = (i - 12) % 12 noise = np.random.randn(4) * 0.05 self.conditions[i] = self.conditions[idx] * (1 + noise) self.cond_mean = self.conditions.mean(axis=0) self.cond_std = self.conditions.std(axis=0) + 1e-8 self.conditions_norm = (self.conditions - self.cond_mean) / self.cond_std self.field_mean = self.fields.mean(axis=(0, 1, 3, 4), keepdims=True) self.field_std = self.fields.std(axis=(0, 1, 3, 4), keepdims=True) + 1e-8 self.fields_norm = (self.fields - self.field_mean) / self.field_std print(f"Dataset: {len(self)} cases") print(f"Conditions shape: {self.conditions_norm.shape}") print(f"Fields shape: {self.fields_norm.shape}") for i, name in enumerate(['phi', 'p0', 'T0', 'Dcj']): print(f" {name}: [{self.conditions_norm[:,i].min():.2f}, {self.conditions_norm[:,i].max():.2f}]") def __len__(self): return len(self.conditions) def __getitem__(self, idx): return { 'condition': torch.tensor(self.conditions_norm[idx], dtype=torch.float32), 'field': torch.tensor(self.fields_norm[idx], dtype=torch.float32), 'is_synthetic': self.is_synthetic[idx] } # ============================================================================= # MODEL # ============================================================================= class RDESurrogate(nn.Module): def __init__(self, condition_dim=4, latent_dim=256, spatial_dim=64): super().__init__() self.condition_encoder = nn.Sequential( nn.Linear(condition_dim, 128), nn.ReLU(), nn.Linear(128, 256), nn.ReLU(), nn.Linear(256, latent_dim), nn.ReLU() ) self.temporal_gen = nn.LSTM( input_size=latent_dim, hidden_size=spatial_dim, num_layers=2, batch_first=True, bidirectional=False ) self.spatial_decoder = nn.Sequential( nn.ConvTranspose2d(spatial_dim, 128, 4, 2, 1, output_padding=(0, 1)), nn.BatchNorm2d(128), nn.ReLU(), nn.ConvTranspose2d(128, 64, 4, 2, 1, output_padding=(1, 1)), nn.BatchNorm2d(64), nn.ReLU(), nn.ConvTranspose2d(64, 32, 4, 2, 1, output_padding=(1, 0)), nn.BatchNorm2d(32), nn.ReLU(), nn.ConvTranspose2d(32, 6, 4, 2, 1), ) self.latent_proj = nn.Linear(spatial_dim, spatial_dim * 9 * 18) def forward(self, condition): B = condition.size(0) z = self.condition_encoder(condition) z_seq = z.unsqueeze(1).expand(-1, 20, -1) temporal_out, _ = self.temporal_gen(z_seq) fields = [] for t in range(20): z_t = temporal_out[:, t, :] z_spatial = self.latent_proj(z_t) z_spatial = z_spatial.view(B, 64, 9, 18) field_t = self.spatial_decoder(z_spatial) fields.append(field_t) return torch.stack(fields, dim=1) # ============================================================================= # TRAINING # ============================================================================= def train_surrogate(): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f"Training on {device}") dataset = RDESurrogateDataset('rde_full_dataset_72cases.npz') real_indices = list(range(12)) synth_indices = list(range(12, 72)) train_synth, val_synth = train_test_split(synth_indices, test_size=12, random_state=42) train_indices = train_synth val_indices = val_synth + real_indices train_set = torch.utils.data.Subset(dataset, train_indices) val_set = torch.utils.data.Subset(dataset, val_indices) train_loader = DataLoader(train_set, batch_size=4, shuffle=True) val_loader = DataLoader(val_set, batch_size=4) print(f"Train: {len(train_set)} cases, Val: {len(val_set)} cases") model = RDESurrogate().to(device) print(f"Parameters: {sum(p.numel() for p in model.parameters()):,}") test_cond = dataset[0]['condition'].unsqueeze(0).to(device) with torch.no_grad(): test_out = model(test_cond) print(f"Test output shape: {test_out.shape}") optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=20) best_val_loss = float('inf') history = [] print(f"\nTraining for 500 epochs...") for epoch in range(500): model.train() train_loss = 0 for batch in train_loader: cond = batch['condition'].to(device) target = batch['field'].to(device) optimizer.zero_grad() pred = model(cond) loss = F.mse_loss(pred, target) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() train_loss += loss.item() train_loss /= len(train_loader) model.eval() val_loss = 0 with torch.no_grad(): for batch in val_loader: cond = batch['condition'].to(device) target = batch['field'].to(device) pred = model(cond) val_loss += F.mse_loss(pred, target).item() val_loss /= len(val_loader) scheduler.step(val_loss) history.append((epoch, train_loss, val_loss)) if (epoch + 1) % 50 == 0: print(f"Epoch {epoch+1}: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}") if val_loss < best_val_loss: best_val_loss = val_loss torch.save({ 'model_state_dict': model.state_dict(), 'cond_mean': dataset.cond_mean, 'cond_std': dataset.cond_std, 'field_mean': dataset.field_mean, 'field_std': dataset.field_std, }, 'rde_surrogate_best.pt') print(f"\nBest val loss: {best_val_loss:.4f}") epochs, train_losses, val_losses = zip(*history) plt.figure(figsize=(10, 5)) plt.plot(epochs, train_losses, label='Train') plt.plot(epochs, val_losses, label='Val') plt.xlabel('Epoch') plt.ylabel('MSE Loss') plt.title('Surrogate Model Training') plt.legend() plt.savefig('surrogate_training.png', dpi=150) plt.close() print("Saved surrogate_training.png") if __name__ == '__main__': train_surrogate()