PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.

Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.

Datasets and code for results presented in the BOON paper

Code for training and inferring acoustic wave propagation in 3D

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.

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

Graph Feedforward Networks: a resolution-invariant generalisation of feedforward networks for graphical data, applied to model order reduction

Official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"

Nonlinear model reduction for operator learning

Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces" (SIREV SIGEST 2024, SISC 2021)

Hyperbolic Learning Rate Scheduler

Benchmarking Surrogates for coupled ODE systems.

Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features'' (NeurIPS 2023, Spotlight)

Project Portfolio

RenONet: Multiscale operator learning for complex social systems

Code required to reproduce results presented in "Probabilistic Operator Learning for Climate Model Parameterisation"

Code for ENM5310 Final Project

Fokker Planck based Data Assimilation method using Fourier Neural Operators as integrator