Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

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A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

An open source model predictive control package for Julia.

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Rigorous function approximation using Taylor models in Julia

Atomic graph models for molecules and crystals in Julia

CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.

Darcy flow and reservoir simulator based on Jutul.jl

Choo-choo

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

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.

Functions to compute omnidirectional landscape connectivity using circuit theory and the Omniscape algorithm.

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.

Robust and optimal design and analysis of linear control systems

Library supporting the ArrayInterface.jl strided array interface.

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

Definition of dynamical systems and integrators for DynamicalSystems.jl

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A Julia package for solving nonlinear differential equations using the harmonic balance method.

The automatically differentiable and GPU-compatible toolkit for CMB analysis.

A foolproof way of doing ML with GUI elements.

Trajectory Optimization for Robot Arms

A few preconditioners for iterative solvers.

Finds relationships between the parameters of a mathematical model

C-code generation and an interface between ControlSystems.jl and SymPy.jl

Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.

Logic Circuits from the Juice library

Neuroimaging in Julia

A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

Pseudo Likelihood Maximization for protein in Julia

Partitioning images into meaningful regions

A CPU- and GPU-friendly package for solving ordinary differential equations

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Measure the distance between two spectra/signals using optimal transport and related metrics

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

Quantum Algorithms for solving differential equations

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Compressed storage for SNP data