Theoretical and Computational physicist
Repositories
SebastianM-C/DynamicExpressions.jl
Ridiculously fast symbolic expressions
SebastianM-C/GPUDiagnostics.jl
SebastianM-C/ComplementaritySolve.jl
SebastianM-C/BaseModelica.jl
Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem
SebastianM-C/DataCollocations.jl
Non-parametric data collocation functionality for smoothing timeseries data and estimating derivatives.
SebastianM-C/MaybeInplace.jl
Rewrite Inplace Operations to be OOP if needed
SebastianM-C/DataInterpolationsND.jl
Interpolation of arbitrarily high dimensional array data
SebastianM-C/SymbolicLimits.jl
SebastianM-C/TupleLU.jl
SebastianM-C/Dedalus.jl
SebastianM-C/PDESystemLibrary.jl
A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia
SebastianM-C/PureUMFPACK.jl
Pure-Julia sparse LU (UMFPACK translation): Gilbert–Peierls + supernodal multifrontal, AMD/COLAMD ordering. SciML-style package; staging repo, transfer to SciML when reviewed.
SebastianM-C/OrdinaryDiffEqOperatorSplitting.jl
Toolbox to handle and solve split formulations of a wide variety of ODE and DAE problems.
SebastianM-C/SparseColumnPivotedQR.jl
Pure-Julia rank-revealing column-pivoted Householder QR for SparseMatrixCSR
SebastianM-C/ADTypes.jl
Repository for automatic differentiation backend types
SebastianM-C/PkgVersionHistory.jl
Find when a julia package version was registered
SebastianM-C/Resolver.jl
SebastianM-C/FindFirstFunctions.jl
Faster `findfirst(==(val), dense_vector)`.
SebastianM-C/LinearOperators.jl
Linear Operators for Julia
SebastianM-C/CountedFloats.jl
SebastianM-C/DataInterpolations.jl
A library of data interpolation and smoothing functions
SebastianM-C/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.
SebastianM-C/BoundaryValueDiffEq.jl
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
SebastianM-C/ModelingToolkit.jl
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
SebastianM-C/PreallocationTools.jl
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
SebastianM-C/LaserTypes.jl
A common interface for different laser types
SebastianM-C/DiffEqReactant.jl
Experimental Reactant compatibe ODE solvers for UDE training on the GPU
SebastianM-C/NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.