A lightweight Consensus-Based Optimisation package for Julia

Tools for solving constrained dynamical systems

A package for modeling and solving Quadratic Program Networks.

(Hierarchical) Phylogenetic Models in Julia

A Package for Estimating Stochastic Frontier Models using Julia

A wrapper for the Python PyDSTool library for the SciML Scientific Machine Learning organization

Battery modelling using Jutul.jl and BattMo in MRST

Controllers for airborne wind energy systems

Library for simulating time binned photons in Waveguide Quantum Electrodynamics

Prescribed flow models for testing cloud microphysics schemes.

Power spectra on the masked sky

Variable Selection with Knockoffs

Experimental Approach to McCormick Relaxation Source-Code Transformation

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

Solutions for combinatorial problems using symbolic methods.

Zeros of Bessel J and Y functions

Nonlinear diffusion problems in Julia

Julia package for MPI reconstruction

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

Differentiable acoustic ray tracers

Package to map SPH particle data to a cartesian grid

Plot critical difference diagrams in Julia

Compute the nearest correlation matrix using a pure Julia implementation

Symmetry-allowed k ⋅ p expansions

Ground-motion simulation via the Stochastic Method

A toolkit for the quantum and classical Dicke model in Julia.

An unofficial implementation of publicly available approximated polynomial models for NASA's Generic Transport Model aircraft.

Hypothesis tests of calibration.

A julia package to compute the Distribution of Relaxation Times (DRT) of a given set of impedance measurements.

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Https://stevengogogo.github.io/FindSteadyStates.jl/dev

A Julia implementation of estimation and validation algorithms for time series compatible with incomplete data.

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

Tools for creating, moving, and discretizing rigid bodies

Causal Inference using Gaussian Processes with Structured Latent Confounders. Estimate treatment effects with Gaussian processes.

A framework to define and output observables and statistics from CliMA simulations

Geometry optimization for molecular simulation

Modular/extensible Julia library for Computer Adaptive Tests

Bayesian addition to Flux.jl

Numerical Methods for Vlasov-Poisson and Vlasov-Maxwell Systems