Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

SciREX is an open-source scientific AI and machine learning framework designed for researchers and engineers by Zenteiq and AiREX lab at IISc Bangalore in partnership with ARTPARK at IISc.

Scientific AI and the Future of OME-Zarr: Building Intelligent Bioimage Analysis Workflows