A general-purpose probabilistic programming system with programmable inference

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 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.

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.

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Pure Julia Implementation of Geometrically Exact Beam Theory

OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.

The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Global glacier model using Universal Differential Equations for climate-glacier interactions

Atomic graph models for molecules and crystals in Julia

Model and solve optimal control problems in Julia

Automated Bayesian model discovery for time series data

Blade Element Momentum Method for Propellers and Turbines

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

E.T. Jaynes home phone.

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An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.

Create parameter estimation problems for ODE models

The current implementation of `LinearAlgebra.eigen` does not support sensitivities. DifferentiableEigen.jl offers an `eigen` function that is differentiable by every AD-framework with support for ChainRulesCore.jl or ForwardDiff.jl.

Optimization framework for nonlinear, gradient-based constrained, sparse optimization problems.

Automates adjoints. Forward and reverse mode algorithmic differentiation around implicit functions (not propagating AD through), as well as custom rules to allow for mixed-mode AD or calling external (non-AD compatible) functions within an AD chain.

Three-dimensional panel method for low-speed aerodynamics

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

Building blocks for simple and advanced particle filtering in Gen.

One stop solutions for all things optimization

The Julia implementation of the generalised hierarchical Gaussian filter

General Relativistic Orbital Dynamics in Julia

GPT-3 as a generative function in Gen.

From Semiconductor to TransistorLevel Modeling in Julia

MeshGraphNets.jl is a software package for the Julia programming language that provides an implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

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A high-performance library for gradient based quantum optimal control

A Julia package for behavioural modeling

Light-weight HistFactory in pure Julia, attempts to be compatible with `pyhf` json format

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Next generation permafrost process modeling in the Julia programming language.

Optimal experimental design of ODE and DAE systems in julia