Python framework that builds spectral-method solvers symbolically (Burns et al. 2020), widely used in fluid dynamics, plasma, star and planet research.
LeVeque team finite-volume package - Godunov-type schemes for hyperbolic conservation laws, standard tool for shocks, tsunami, reservoir, and seismic waves.
FEM platform where you write variational forms in near-math UFL syntax and discretization/solving is automated - the standard learning route with its official tutorial book.
Julia SciML ecosystem library unifying PINN/DeepONet/NeuralOperator solvers with seamless DifferentialEquations.jl interoperability and GPU/autodiff out of the box.
NVIDIA open-source physics-AI training/inference framework (formerly Modulus) with official examples across CFD, structural, EM, weather, and semiconductor TCAD.
The Python analysis library behind Materials Project - crystal structures, phase diagrams, band structures, DOS out of the box; the standard data layer between DFT and ML workflows.
This roadmap reviews the evolution of polymer informatics toward on-demand inverse design, causal reasoning across chemistry, processing, and performance, and closed-loop autonomous experimentation.
See LICENSE filepolymer informaticsinverse designautonomous experimentation
This resource reviews point-cloud generative fast-simulation methods for photon and hadronic showers in highly granular calorimeters, addressing collider simulation across different particles and detector geometries.
See LICENSE fileCalorimeter SimulationPoint-Cloud GenerationCollider Experiments
HuggingFace blog tutorial on using NVIDIA Warp physics framework and MjWarp bridge for robotics simulation, covering rigid body dynamics and contact mechanics in learning workflows.
See LICENSE fileRobotics SimulationNVIDIA WarpMjWarp
A focused survey of PINN improvements via transfer learning and meta-learning, addressing PINN's notorious convergence difficulty and the need to re-optimize on every PDE parameter change - the engineering reference for productionizing PINNs.
arXiv preprint (copyright held by authors)PINNTransfer LearningMeta Learning
A survey on embedding physics priors in robot learning, covering how physical constraints like contact mechanics, rigid-flexible coupling, and multibody dynamics improve sample efficiency and reliability of learned policies.
See LICENSE filerobot learningphysics priorsmultibody dynamics
NVIDIA tutorial demonstrating GPU acceleration techniques for ROS 2 computational graphs using Isaac ROS and AI Agent integration to speed up robotics workloads.
See LICENSE fileROS 2NVIDIA Isaac ROSRobotics Acceleration
A survey of data-driven and physics-informed neural operators, organizing the architecture landscape (DeepONet / FNO / GNO / PINO / Transformer-based / diffusion) along three axes: scalability, generalizability, and data sensitivity.
arXiv preprint (copyright held by authors)Neural OperatorScientific MLDeepONet
A practical focused review of neural operators covering DeepONet, FNO and PCANet with hands-on training strategies; benchmarked on Poisson and linear elasticity, and demonstrated as surrogates for Bayesian inversion - an engineering-ready primer.
arXiv preprint (copyright held by authors)Neural OperatorDeepONetFNO
