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
The most beginner-friendly PINN framework, with excellent Chinese docs and TensorFlow/PyTorch backends - the top choice to start with physics-informed networks.
GNU Lesser General Public License v2.1PINNBeginnerChinese Docs
Presents AKAPINN, a mesh-free KAN-PINN method for nonlinear strain-limiting PDEs with exact hard boundary ansatz and pseudo-Huber loss, evaluated across three material-parameter pairs.
See LICENSE filePINNKolmogorov-Arnold NetworkQuasilinear PDE
A unified framework combining physics-informed neural networks with finite element methods via functional Gauss-Newton problems and Petrov-Galerkin discretization.
See LICENSE filePINNfinite element methodPDE solver
A HuggingFace-hosted model repository for bubble dynamics using Physics-Informed Neural Networks (PINN), with the model name indicating focus on PINN-based bubble dynamics applications.
A joint training approach that addresses neural operator out-of-distribution degradation and PINN multi-solution convergence errors by leveraging imperfect neural operators to guide PINN optimization.
PhD course lecture notes from University of Bozen/Bolzano covering Physics Informed Neural Networks and Neural Operators with PyTorch implementation tutorials.
A free online textbook from TU Munich covering physics + deep learning: differentiable physics, PINNs, neural operators and turbulence modeling, with runnable code.
A HuggingFace repository hosting a physics-informed neural network model for computational fluid dynamics, implementing physics constraints embedded within neural network architectures.
See LICENSE filePINNCFDphysics-informed neural network
A classic Chinese structured tutorial implementing PINN from minimal dependencies, paired with Raissi's original repo - the best start for Chinese-speaking learners.
A systematic review of 2025-2026 progress in physics-informed ML - neural operators, diffusion for PDE solving, large benchmarks and industrial deployment.
NVIDIA's physics-ML training framework (formerly Modulus) with PINN, neural operators and physics-constrained training - commercial-friendly, industry-first choice.
A periodic physics-informed neural network (P-PINN) framework is proposed for solving parametric periodic flows and analyzing finite-amplitude aeroelastic responses in a purely physics-driven manner.
