Framework Paper / Neural Operator
NVIDIA's PINN framework built on neural operators (FNO/DeepONet/AFNO) for end-to-end differentiable physics simulation, with multi-GPU training and deployment.
Apache-2.0 NVIDIANeural OperatorFramework
Annual Survey / Physics-Informed Learning
A systematic review of 2025-2026 progress in physics-informed ML - neural operators, diffusion for PDE solving, large benchmarks and industrial deployment.
Various SurveyPhysics-InformedPINN
Frontier Paper
Proposes a physics-attention mechanism that projects meshes onto learnable physical-state tokens, improving efficiency and accuracy of PDE solving on large industrial geometries.
arXiv preprint (copyright held by authors) TransformerGeometryIndustrial
Foundational Paper
Presents a branch-trunk architecture realizing a deep learning version of the universal operator approximation theorem - the first systematic architecture for learning nonlinear operators and still a mainstream baseline.
arXiv preprint (copyright held by authors) DeepONetNeural OperatorFoundational
Foundational Paper
Introduces neural operators with integral kernels parameterized in Fourier space, enabling resolution-invariant PDE solving - a foundational and highly cited work in physics AI.
arXiv preprint (copyright held by authors) FNONeural OperatorFoundational
Must-Read Benchmark Paper
Proposes a unified benchmark across 16 physical domains, arguing for cross-domain pretraining gains - a must-read for physics foundation models.
Paper (see publication page) BenchmarkPhysics Foundation ModelSurvey