Scientific Machine Learning Tutorials
Scientific machine learning tutorials connecting PyTorch autograd, differentiable models, neural operators, and physics-informed neural networks.
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Machine Learning Training from Scratch: Loss, Gradients, and Overfitting
Hand-derive the gradient, write the training loop in NumPy, then again in PyTorch. Watch a deliberately oversized model overfit, then fix it three ways.
Reproduce the classic train-loss-down, validation-loss-up overfitting curve and learn what fixes it.
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Neural Networks from One Neuron to a PyTorch MLP Classifier
One neuron to matrix layer to PyTorch abstraction to an MLP that fits a sine wave, then a real binary classifier on Wisconsin breast cancer data.
See exactly when one neuron fails, why a hidden layer works, and how the same pattern becomes a classifier.
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PINN Tutorial in PyTorch: Damped Oscillator, Autograd, and Inverse Problems
Data, physics, or both? A damped oscillator compares all three on the same architecture, then bridges to inverse problems and neural operators.
Use torch.autograd.grad to make a neural network obey an ODE, then turn the same idea into an inverse problem.
