A Julia package for parameterized convex approximators including parameterized log-sum-exp (PLSE) network.

A scatter operation library for neural network

Signal temporal logic (STL) formulas and robustness in Julia

Local anisotropies and nonstationary spatial processes for the GeoStats.jl framework

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Find timezone from latitude and longitude

Neural-Network Outlier Detection Algorithms for Julia

(Hierarchical) Phylogenetic Models in Julia

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Built-in solvers for the GeoStats.jl framework

Locally weighted regression solver for the GeoStats.jl framework

GraphNetCore.jl is a software package for the Julia programming language that provides an the core functionality of the MeshGraphNets.jl package. Some parts are based on the implementation of the MeshGraphNets framework by Google DeepMind for simulating mesh-based physical systems via graph neural networks.

DNA Motif discovery that includes the discovery of flexible (long or gapped) motifs.

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Utilities to read/write extended GSLIB files in Julia

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

High performance hybrid Monte Carlo simulation

Calculates multidimensional functions faster by exploiting their separability.

A Julia package for modelling Algorithmic Recourse Dynamics.

Jet operator pack for seismic modeling dependent on WaveFD.jl. Part of the COFII framework.

Layerwise Relevance Propagation in Julia.

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

A Julia based implementation of Plug and Play Language Models

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

Bayesian addition to Flux.jl

Point patterns for the GeoStats.jl framework

Evidential Deep Learning Layers for Flux

A small Julia package to facilitate working with the Trillion Dollar Words dataset.

Reliability-based optimization

A Julia package for integration of packages of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

A powerful, no code solution for control and systems engineering

Julia implementation of the ISOKANN algorithm for the computation of invariant subspaces of Koopman operators

Subspace Inference package for uncertainty analysis in deep neural networks and neural ordinary differential equations using Julia

Compressed belief-state MDPs in Julia compatible with POMDPs.jl

A Julia package for people that love to learn new things about constraints

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Data-driven models for extremely precise radial velocity (EPRV) spectra

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Differential Inequality Algorithms for Parametric ODEs