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

Code for Rice et al. 2020 "Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forceasting"

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

WinDiNet: Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows

Advisory water treatment for industrial cooling towers: risk indices, anomaly detection, forecasting and dose recommendations. A human authorizes every dose.

The official code repository of the paper "An effective physics-informed neural operator framework for predicting wavefields" " .

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.

Official code for arXiv:2604.11807 - Physics-Informed State Space Models for Off-Grid Solar Forecasting

Source code of the Paper "Physics-Informed Generative Modeling of Wireless Channels" (ICML 25)

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

Open-source Physics-informed Turbojet Digital Twin Platform for Engine Health Monitoring, Prognostics, Explainable AI, and Interactive 3D Visualization.

Ununennium (Element 119, the next alkali metal) represents the cutting edge of satellite imagery machine learning. This library provides a unified, GPU-first framework for end-to-end Earth observation workflows, from cloud-native data access through model training to deployment.

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

Neural ODE-based State of Charge (SOC) estimation for Li-ion batteries using the NASA Battery Dataset. Built as a weekend project to explore learned dynamics for battery modeling, with visualizations designed for engineering audiences.

A differentiable spatiotemporal manifold for thermodynamic material evolution, built in pure Rust on the Burn tensor framework.