Physics Informed Learning, Neural Networks, Machine Learning, Differential Equations, Math.
wider range of times epsilon = torch.normal(0,0.1, size=(len(t),1)).float() t_train = t + epsilon # Forward pass y_pred = model(t_train) # Calculate the derivative of the forward pass w.r.t. the input (t) dy_dt = torch.autograd.grad(y_pred, t_train, grad_outputs=torch.ones_like(y_pred),