Physics-Informed Neural networks for Advanced modeling

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

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

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

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测

RVAV: a physics-informed PyTorch optimizer for energy-stable, high-LR training—with a simple closure API, tests, CI, and quickstart.

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

Data-driven discovery of PDEs using the Adjoint method