Create aeronautics_ai.yaml

Signed-off-by: Gilbert Algordo <69397216+gilbertalgordo@users.noreply.github.com>

Author: gilbertalgordo

aeronautics_ai.yaml

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class AeroNet(nn.Module):
+    def __init__(self):
+        super(AeroNet, self).__init__()
+        # Input: 13 features (Position, Velocity, Orientation, Control Inputs)
+        # Output: 6 features (Forces and Moments: Fx, Fy, Fz, L, M, N)
+        self.network = nn.Sequential(
+            nn.Linear(13, 64),
+            nn.ReLU(),
+            nn.Linear(64, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64),
+            nn.Sigmoid(), # Constraints output range
+            nn.Linear(64, 6)
+        )
+
+    def forward(self, x):
+        return self.network(x)
+
+# Initialize model
+model = AeroNet()
+print(model)
+
+
+
+import torch
+import torch.nn as nn
+
+class FlightPINN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(4, 50), nn.Tanh(),
+            nn.Linear(50, 50), nn.Tanh(),
+            nn.Linear(50, 3) # Predicts Cl, Cd, Cm (Lift, Drag, Moment)
+        )
+
+    def forward(self, alpha, mach, reynolds, alt):
+        inputs = torch.cat([alpha, mach, reynolds, alt], dim=1)
+        return self.net(inputs)
+
+def physics_loss(model, inputs, targets):
+    pred = model(inputs)
+    mse_loss = nn.MSELoss()(pred, targets)
+    
+    # Physics Constraint: Drag must be non-negative and generally parabolic with alpha
+    # We penalize the model if it predicts negative drag (physical impossibility)
+    drag_constraint = torch.mean(torch.relu(-pred[:, 1])) 
+    
+    return mse_loss + (0.1 * drag_constraint)
+
+
+
+import gym
+from stable_baselines3 import PPO
+
+# Custom Aero Environment (Abstracted)
+class AeroEnv(gym.Env):
+    def step(self, action):
+        # 1. Apply thrust and surface deflections
+        # 2. Calculate new State (x, y, z, phi, theta, psi)
+        # 3. Reward = distance_to_waypoint - (fuel_consumption * k)
+        pass
+
+model = PPO("MlpPolicy", "AeroEnv-v0", verbose=1)
+model.learn(total_timesteps=100000)