Wavelet Neural Operator for solving parametric partialdifferential equations in computational mechanics problems

Implementation of Physics-Informed Diffusion Models

[ICLR2025] Kolmogorov-Arnold Transformer

历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.

[ICLR2025] Wavelet Diffusion Neural Operator (WDNO) uses diffusion models for generative PDE simulation and control.

PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations

Characterizing possible failure modes in physics-informed neural networks.

Enhancing spatial understanding in text-to-Image diffusion models

Domain Agnostic Fourier Neural Operators (DAFNO)

Solving High Dimensional Partial Differential Equations with Deep Neural Networks

Official implementation of Scalable Transformer for PDE surrogate modelling

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Generative Pre-Trained Physics-Informed Neural Networks Implementation

PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations

Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.

PDE Preserved Neural Network

Official implementation of NeurIPS 2024 paper "DiffusionPDE: Generative PDE-Solving Under Partial Observation"

PyTorch implementations of Learning Mesh-based Simulation With Graph Networks

Investigating PINNs

DeepXDE and PINN

PDEBench: An Extensive Benchmark for Scientific Machine Learning

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Learning in infinite dimension with neural operators.