Differentiable acoustic ray tracers

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Http://proceedings.mlr.press/v119/bruinsma20a.html

Geometry optimization for molecular simulation

A Julia package for piecewise spectral methods such as p-FEM

Linear fractional programming with Julia and JuMP

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Extra non essential functionality for MixedModels.jl

Julia package for football analytics

Julia implementation of the Gumbel-Softmax reparametrization trick compatible with Zygote and ForwardDiff

Zeros of Bessel J and Y functions

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Traits and low-level utilities for mass spectrometry

Definitions of the Julia types for simulating an ISO's market clearing.

Support for out-of-core sparse arrays in Julia, backed by a HDF5 file stored on disk

BioCCP.jl exploits the Coupon Collector Problem for sample size determination in combinatorial biotechnology.

Extension of PowerSimulations.jl to model Hydropower devices

Point-based value iteration solver for POMDPs

Nonlinear diffusion problems in Julia

Julia package for visualizing chords on the guitar fretboard.

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Interface and Julia implementation of exchange-correlation functionals

Effective parallelism for power flow calculation

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

Scientific machine learning glacier mass balance models

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A small Julia tool for visualizing DICOM metadata.

Compute the nearest correlation matrix using a pure Julia implementation

Optimised multi-node MPI sorting algorithms in Julia

Julia package for MPI reconstruction

Theorems Environment for Franklin.jl

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Algorithms for performing angular differential imaging (ADI)

Graphical Analysis of Structural Causal Models

GraphBLAS backed graphs for LightGraphs.jl

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

A package that stores Constraint Programming models for JuliaConstraints solvers

A Makie theme

A package to handle the domain of variables in JuliaConstraints packages.

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