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

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

CEED Library: Code for Efficient Extensible Discretizations

Atomic graph models for molecules and crystals in Julia

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

The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms

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.

WebGL-based 3D visualizer in Julia

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.

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

Simple function tracing for Julia

Differentiable collision detection for polytopes, capsules, cylinders, cones, spheres, and polygons.

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Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

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

A differentiable physics engine for robotics

Simple predictive models for tumor growth, and tools to apply them to clinical data

Optimal experimental design of ODE and DAE systems in julia

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

Trajectory Optimization for Robot Arms

A development environment for robust and global optimization

General Relativistic Orbital Dynamics in Julia

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

A high-performance library for gradient based quantum optimal control

From Semiconductor to TransistorLevel Modeling in Julia

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

Simulation and visualization of articulated rigid body systems in Julia

What if your code allocated less? Remember what memory we needed last time and use it again every time after

Causal, Higher-Order, Probabilistic Programming

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.

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.

A Julia package for regression in the sphere based on Universal Differential Equations

Package for fitting models according to the deep compartment modeling framework for pharmacometric applications.

A set of tools to quickly generate and visualize autonomous driving scenarios for Algames.jl.

Maintainable type inference

Unified contact simulaton and collision detection

An easy to use Julia framework for agent based modeling.

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