Physics-Informed Neural networks for Advanced modeling

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs

Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements".

Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics

SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance

TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).

Physics-informed information field theory - Solve inverse problems with built-in model form uncertainty estimation

Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn

Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127

UQ Group (Director: Hadi Meidani)

GIC Forecasting & Analysis — A data-driven framework for modeling Geomagnetically Induced Currents using solar wind data, SuperMAG observations, and advanced ML techniques. Integrates domain physics, feature engineering, and imputation strategies to tackle real-world space weather challenges.

Short review of Physics-Informed ML/DL

Data-driven discovery of PDEs using the Adjoint method

Bart.dIAs is the WebGRIPP Project's Coding Assistant. Currently it is a simple (parallel) coding assistant.