NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.

Testing PINNS with ODE

A playground repo for testing some Physics-informed Neural Networks (PINNs) for solving simple differential equations.

I am testing the PINNs algorithm to infer hidden parameters in PDEs using neural networks.

All the training runs we do when testing the PINNs out.

PINN testing

PINN model testing

Testing PINNS on Fluid Dyamics

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Testing and visualization pipeline for Physics-Informed Neural Networks on synthetic thermal ablation data

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Component Level IC Design, Testing, PINN and Datasheet

Testing for the use of PINN's with SlicerTMS

PINN tutorial for testing the water flowing inside a pipe

Testing the training speedups from using a conservative pinn for conservative vector field problems

Testing Trajectory prediction and using it with guidance in a computationally lightweight manner using PINN.

My personal sandbox for learning new programming languages and frameworks. Currently exploring jax, PINNs, testing, CI/CD and so on.

Github repository for UCL AI societys PINNS for Quantitative Finance. Here will contain development and testing of models and saves of or links to data sets

Comprehensive solver for the Fractional Nonlinear Klein-Gordon Equation (FNKGE) modeling emergent quantum mechanics. Benchmarks PINNs (Fourier features & spectral fractional Laplacian) against topology-aware PI-GNNs. Features deterministic agent simulations, falsifiability testing, and linear stability analysis.