AstitvaAggarwal/FindFirstFunctions.jl
Faster `findfirst(==(val), dense_vector)`.
Repositories
AstitvaAggarwal/FunctionWrappersWrappers.jl
AstitvaAggarwal/ValueAndGradient.jl
A general Julia AD interface for calculating the VJP and JVP
AstitvaAggarwal/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
AstitvaAggarwal/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.
AstitvaAggarwal/Lux.jl
Elegant and Performant Deep Learning
AstitvaAggarwal/NNlib.jl
Neural Network primitives with multiple backends
AstitvaAggarwal/Mooncake.jl
language level autograd compiler for Julia
AstitvaAggarwal/Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
AstitvaAggarwal/ADTypes.jl
Repository for automatic differentiation backend types
AstitvaAggarwal/DataInterpolations.jl
A library of data interpolation and smoothing functions
AstitvaAggarwal/Integrals.jl
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
AstitvaAggarwal/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
AstitvaAggarwal/Mjolnir.jl
A little less conversation, a little more abstraction
AstitvaAggarwal/ComponentArrays.jl
Arrays with arbitrarily nested named components.
AstitvaAggarwal/LinearSolve.jl
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
AstitvaAggarwal/DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
AstitvaAggarwal/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)
AstitvaAggarwal/NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
AstitvaAggarwal/julialabs.github.io
JuliaLab Website
AstitvaAggarwal/code-base-investigator
An analysis tool providing insight into the portability and maintainability of an application’s source code.
AstitvaAggarwal/DifferentiationInterface.jl
An interface to various automatic differentiation backends in Julia.
AstitvaAggarwal/LinearAlgebra
codes of many methods i am learning in college
AstitvaAggarwal/julia
The Julia Programming Language
AstitvaAggarwal/computational_aging_course
Free online course on computational biology of aging driven by Jupyter book and supported by Skoltech.
AstitvaAggarwal/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
AstitvaAggarwal/ultrack
Cell tracking and segmentation software
AstitvaAggarwal/newSegPipline
implement a Cellpose clone to segment nuclei
AstitvaAggarwal/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
AstitvaAggarwal/AdvancedHMC.jl
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms