Sathvik Bhagavan's repositories
OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
ChainRulesCore.jl
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.
DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
DataInterpolations.jl
A library of data interpolation and smoothing functions
DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
DiffEqFlux.jl
Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™
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 and simulate FMUs seamlessly inside the Julia programming language!
General
The official registry of general Julia packages
GlobalSensitivity.jl
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
HighDimPDE.jl
A Julia package that breaks down the curse of dimensionality in solving PDEs.
Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
JumpProcesses.jl
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
MethodOfLines.jl
Automatic Finite Difference PDE solving with Julia SciML
ML_course
EPFL Machine Learning Course, Fall 2024
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
ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
MultiScaleArrays.jl
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
ParameterEstimation.jl
ParameterEstimation.jl is a Julia package for estimating parameters and initial conditions of ODE models given measurement data.
PDEBase.jl
Common types and interface for discretizers of ModelingToolkit PDESystems.
PDESystemLibrary.jl
A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia
PreallocationTools.jl
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
sathvikbhagavan
Config files for my GitHub profile.
sciml.ai
The SciML Scientific Machine Learning Software Organization Website
SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
SciMLDocs
Global documentation for the Julia SciML Scientific Machine Learning Organization
Surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
Symbolics.jl
Symbolic programming for the next generation of numerical software