A high-performance code for simulating hierarchical multistar systems.
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Updated
Jun 19, 2024 - Julia
A high-performance code for simulating hierarchical multistar systems.
Code Listings models of The Years of the Switch and the Dream of The Singularity 2020 CE
Solving Universal Differential Equations in Julia
Fast and flexible glacier ice flow models
A helper repository for diffeqpy to enable high-performance differential equation solving scientific machine learning (SciML) in Python
Solvers for Stokes-type equations and saddle-point problems for scientific machine learning (SciML)
Wrappers for arrays to make broadcasted operations multithreaded and multiprocessed for high-performance scientific machine learning (SciML)
Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem
Dirac operators for lattice QCD with Julia
Binary library builder for Sundials for the SciML scientific machine learning open source software organization
Painting the phase portrait of random and deterministic systems
Diagrams and visualizations for scientific machine learning (SciML)
Solvers for finite element discretizations of PDEs in the SciML scientific machine learning ecosystem
Saving and loading of JuliaDiffEq types for I/O of scientific machine learning (SciML)
A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
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 wrapper for the Python PyDSTool library for the SciML Scientific Machine Learning organization
A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)
Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers
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