Pinned Repositories
All-In-One-Image-Dehazing-Tensorflow
Deep learning based image dehazing
AODNet
AODnet-by-pytorch
Image Dehaze, Pytorch, An All-in-One Network for Dehazing, AOD-Net
CS350A_Assignments
Group_7_CS335_Project
Group Project for 'Compiler Design', Spring 2022
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)
Parallel-Incompressible-Flow-FD-Solver
Recommendation_system
SAT-Solver
Sudoku_Pair_Solver_Generator
sathvikbhagavan's Repositories
sathvikbhagavan/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)
sathvikbhagavan/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.
sathvikbhagavan/DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
sathvikbhagavan/DataInterpolations.jl
A library of data interpolation and smoothing functions
sathvikbhagavan/DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
sathvikbhagavan/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
sathvikbhagavan/FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™
sathvikbhagavan/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!
sathvikbhagavan/GlobalSensitivity.jl
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
sathvikbhagavan/HighDimPDE.jl
A Julia package that breaks down the curse of dimensionality in solving PDEs.
sathvikbhagavan/Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
sathvikbhagavan/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)
sathvikbhagavan/Lux.jl
Explicitly Parameterized Neural Networks in Julia
sathvikbhagavan/MethodOfLines.jl
Automatic Finite Difference PDE solving with Julia SciML
sathvikbhagavan/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
sathvikbhagavan/ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
sathvikbhagavan/MultiScaleArrays.jl
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
sathvikbhagavan/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
sathvikbhagavan/NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
sathvikbhagavan/Optimization.jl
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
sathvikbhagavan/ParameterEstimation.jl
ParameterEstimation.jl is a Julia package for estimating parameters and initial conditions of ODE models given measurement data.
sathvikbhagavan/PDESystemLibrary.jl
A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia
sathvikbhagavan/PreallocationTools.jl
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
sathvikbhagavan/sathvikbhagavan
Config files for my GitHub profile.
sathvikbhagavan/sciml.ai
The SciML Scientific Machine Learning Software Organization Website
sathvikbhagavan/SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
sathvikbhagavan/SciMLDocs
Global documentation for the Julia SciML Scientific Machine Learning Organization
sathvikbhagavan/Surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
sathvikbhagavan/Symbolics.jl
Symbolic programming for the next generation of numerical software
sathvikbhagavan/TimeseriesSurrogates.jl
A Julia library for generating surrogate data.