lmikelsons

lmikelsons's Stars

• una-auxme/una-auxme.github.io

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• ThummeTo/FMIBase.jl

  FMIBase.jl provides the foundation for the Julia packages FMIImport.jl and FMIExport.jl.

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• una-auxme/GraphNetCore.jl

  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.

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• una-auxme/MeshGraphNets.jl

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

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• ThummeTo/FMISensitivity.jl

  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

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• ThummeTo/DistributedHyperOpt.jl

  DistributedHyperOpt.jl is a package similar to HyperOpt.jl, but explicitly focusing on distributed (multi-processing) hyperparameter optimization by design.

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• ThummeTo/DifferentiableEigen.jl

  The current implementation of `LinearAlgebra.eigen` does not support sensitivities. DifferentiableEigen.jl offers an `eigen` function that is differentiable by every AD-framework with support for ChainRulesCore.jl or ForwardDiff.jl.

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• dzimmer/PlanarMechanics

  A free Modelica library for planar mechanical multi-body systems

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• mitmath/18330

  18.330 Introduction to Numerical Analysis

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• ThummeTo/ForwardDiffChainRules.jl

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• stefanradev93/BayesFlow

  A Python library for amortized Bayesian workflows using generative neural networks.

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• SciML/SciMLSensitivity.jl

  A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

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• JuliaLang/julia

  The Julia Programming Language

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• ThummeTo/FMIBuild.jl

  FMIBuild.jl holds dependencies that are required to compile and zip a Functional Mock-Up Unit (FMU) compliant to the FMI-standard (fmi-standard.org). Because this dependencies should not be part of the compiled FMU, they are out-sourced into this package. FMIBuild.jl provides the build-commands for the Julia package FMIExport.jl.

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• ThummeTo/FMIExport.jl

  FMIExport.jl is a free-to-use software library for the Julia programming language which allows for the export of FMUs (fmi-standard.org) from any Julia-Code. FMIExport.jl is completely integrated into FMI.jl.

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• ThummeTo/FMIImport.jl

  FMIImport.jl implements the import functionalities of the FMI-standard (fmi-standard.org) for the Julia programming language. FMIImport.jl provides the foundation for the Julia packages FMI.jl and FMIFlux.jl.

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• SciML/DiffEqFlux.jl

  Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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• SciML/DifferentialEquations.jl

  Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

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• ThummeTo/FMICore.jl

  FMICore.jl implements the low-level equivalents of the C-functions and C-data types of the FMI-standard (fmi-standard.org) for the Julia programming language.

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• ThummeTo/FMI.jl

  FMI.jl is a free-to-use software library for the Julia programming language which integrates FMI (fmi-standard.org): load or create, parameterize, differentiate and simulate FMUs seamlessly inside the Julia programming language!

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• ThummeTo/FMIFlux.jl

  FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

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• ModiaSim/TinyModia.jl

  Deprecated package (use instead Modia.jl)

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• ORNL-Modelica/UnrealEngine-FMIPlugin

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• ModiaSim/Modia.jl

  Modeling and simulation of multidomain engineering systems

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• CATIA-Systems/FMIKit-Simulink

  Import and export Functional Mock-up Units with Simulink

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