This repository contains Jupyter notebooks from my learning journey with Physics-Informed Neural Networks (PINNs). These are not complete projects but serve as educational resources to explore core and intermediate concepts in applying neural networks to solve partial differential equations (PDEs).

Fine-tuned DeepCFD UNet surrogate model for CFD flow field prediction

Physics-aware machine learning study for event classification in high-energy physics.

FNO-RC: Fourier Neural Operator with Conformal Residual Coupling for PDEs

This repository contains a lightweight CNN-based approach for top-quark jet classification, using data from CERN’s public Zenodo dataset.

Mechanistic interpretability of classical neural networks trained on quantum data — using sparse autoencoders to detect quantum structure in classical representations.

σFlow-PDE: A drop-in H-Bar training engine that escapes the σ-trap in neural PDE solvers via live σ/δ/α ODE integration, autonomous phase curriculum, and auto-falsification.

Predict steady-state 2D laminar flow fields in milliseconds using a deep learning surrogate model for Computational Fluid Dynamics.