Automatically benchmark functions!

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

Atomic graph models for molecules and crystals in Julia

A powerful, no code solution for control and systems engineering

Dynamical systems identification

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

-

Visualization for the ConstrainedDynamics.jl package.

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

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

A package for the development and implementation of continuous-time policy gradient (CTPG) methods.

Count operations for Julia code

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

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.

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

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

-

A differentiable physics engine for robotics

Bounds and Relaxations of Parametric Differential Equations

Abstraction Layer for Dynamic Bounds

Valid Discrete-Time Methods for Relaxing pODEs

Differential Inequality Algorithms for Parametric ODEs

Optimal experimental design of ODE and DAE systems in julia

A development environment for robust and global optimization

An easy to use Julia framework for agent based modeling.

A Julia package containing GNC algorithms for autonomous aerospace systems

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.

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

-

A SciML symbolic-numeric compiler tool for investigating functions in order to perform optimizations

Estimate the absolute performance of a piece of Julia code

A Julia package for UI development

-

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

Maintainable type inference

Lens.jl is a simple Julia library to inspect the runtime behaviour of your programs, with minimal interference to the program itself.

CEED Library: Code for Efficient Extensible Discretizations

A cassette-based debugger | The Other Debugger

WebGL-based 3D visualizer in Julia