Systems

Physics-Informed Neural Networks

I wanted a partial differential equation on the GPU, with the equation inside the loss. These notebooks are CUDA C++ and PyTorch experiments on Navier–Stokes, Burgers, and related nonlinear equations.

Forty-four stacked ink curves of Burgers’ equation, from a smooth sine wave at t = 0 to a sharp shock at x = 0 by t = 1. — Fig. 1Burgers’ equation with ν = 0.01/π, drawn one time slice at a time: the sine wave steepens into a shock at x = 0. Exact solution by the Cole–Hopf transform, computed for this figure. It is the benchmark PINNs are fitted to, not output from the repository’s networks.

Stack: CUDA, PINNs, CFD, Scientific Computing
Links: Code

Also

2026CUDA Blackwell Labs
2026Agentic structure-from-motion
2026PCOS edge agent
2024Dalton Mills VR Reconstruction
2023AWS AI/ML Scholar