PDEBench: An Extensive Benchmark for Scientific Machine Learning

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

Official PyTorch implementation of PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Open source code for ICML 2025 Paper: Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias

Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem

A set of tools for developing new methods and techniques in physics informed neural networks written in jax.

Stochastic PDE solvers (SPDE) built on top of exponax: Exponential Euler-Maruyama stepper for the stochastic Allen-Cahn equation with additive/multiplicative Q-Wiener noise, tamed nonlinearities, ensemble utilities, Richardson extrapolation, and a Strang-split hybrid SSA scaffold.

Scientific Machine Learning in solid mechanics: PINNs vs FEM vs black-box models on a 1D bar.

This repository contains code to accompany the paper "Near-optimal Sketchy Natural Gradients for PINNs".

Backend-Agnostic Inverse 1D Burgers Solver via Tesseract (Viscosity Estimation with JAX and PyTorch PINNs)

(CIKM '25 Oral) Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE

A unified scientific machine learning framework built on JAX/Flax NNX

Automated calibration of RANS turbulence models for hypersonic flows using SciML. Achieved 26% RMSE reduction at Mach 14.

Phase field modelling of Thermodynamic equations via Physics-Informed Neural Networks(PINNs)

This project implements a PINN using TensorFlow to solve a 2D steady-state convection–diffusion PDE on a unit square by minimizing the PDE residual and enforcing Dirichlet boundary conditions. It demonstrates domain sampling, differentiation, constrained training, and inference on unseen test points without requiring labeled solution data.

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics

Repositório oficial do artigo "Otimização de operações de E/S em aplicações científicas de aprendizado de máquina guiadas pelo Drishti" (ERAD). Código-fonte, scripts de perfilamento e resultados.