Dirac operators for lattice QCD with Julia
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Updated
Mar 5, 2024 - Julia
Dirac operators for lattice QCD with Julia
A high-performance code for simulating hierarchical multistar systems.
Solving Universal Differential Equations in Julia
A collection of chaotic ODEs.
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 high-performance library for gradient based quantum optimal control
Code Listings models of The Years of the Switch and the Dream of The Singularity 2020 CE
Optimal experimental design of ODE and DAE systems in julia
Painting the phase portrait of random and deterministic systems
Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
Julia implementation of the Earth4All model using the WorldDynamics framework.
Diagrams and visualizations for scientific machine learning (SciML)
Fast and flexible glacier ice flow models
Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)
Repository for the Control of Stochastic Quantum Dynamics with Differentiable Programming paper.
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
Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)
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