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25 search results found

Differentialequations.jl ⭐ 2,689

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

Deepxde ⭐ 2,135

A library for scientific machine learning and physics-informed learning

Scimltutorials.jl ⭐ 698

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Diffeqpy ⭐ 456

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

Scimlbenchmarks.jl ⭐ 279

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Koopmanlab ⭐ 138

A library for Koopman Neural Operator with Pytorch.

Idrlnet ⭐ 129

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs

Tensordiffeq ⭐ 60

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Python Suite for Advanced General Ensemble Simulations

Deep_learning_for_dynamical_systems ⭐ 31

Encoding physics to learn reaction-diffusion processes

Reactorch ⭐ 21

A Differentiable Reacting Flow Simulation Package in PyTorch

DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations

Scipydiffeq.jl ⭐ 20

Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization

Deep_kolmogorov ⭐ 18

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

Physics-informed deep super-resolution of spatiotemporal data

Rom Opinf Combustion 2d ⭐ 15

Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.

Tensorflowdiffeq.jl ⭐ 14

Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)

A Python library for training neural ODEs.

Scimlbenchmarksoutput ⭐ 13

SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance

A Tutorial for Parallelised Scalable Simulations in TensorFlow

[AAMAS 2023] Differentiable Agent-based Epidemiology

Adv Optimization Physics Py ⭐ 6

🌌🛩 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗳𝗼𝗿 𝗽𝗵𝘆𝘀𝗶𝗰𝘀: 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻, 𝗼𝗿𝗱𝗶𝗻𝗮𝗿𝘆, 𝗮𝗱𝘃𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗵𝘆𝗽𝗲𝗿𝗯𝗼𝗹𝗶𝗰 𝗽𝗮𝗿𝘁𝗶𝗮𝗹 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗲𝗾𝘀

Calibrating Summaries ⭐ 5

This is the official PyTorch codebase for the ACL 2023 paper: "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization".

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