Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
NeuralLyapunov.jl
A library for searching for neural Lyapunov functions in Julia
DeepEquilibriumNetworks.jl
Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence
AbstractCosmologicalEmulators.jl
Repository containing the abstract interface to the emulators used in the CosmologicalEmulators organization
ContinuousNormalizingFlows.jl
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
Sophon.jl
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
NeuralGraphPDE.jl
Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks
Solaris.jl
Lightweight module for fusing physical and neural models
Boltz.jl
Accelerate your ML research using pre-built Deep Learning Models with Lux
GeometricMachineLearning.jl
Structure Preserving Machine Learning Models in Julia
