Topic: scientific-machine-learning Goto Github
Some thing interesting about scientific-machine-learning
Some thing interesting about scientific-machine-learning
scientific-machine-learning,Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
User: chrisrackauckas
Home Page: https://arxiv.org/abs/2001.04385
scientific-machine-learning,IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
Organization: idrl-lab
scientific-machine-learning,Arrays with arbitrarily nested named components.
User: jonniedie
scientific-machine-learning,Probabilistic Programming and Nested sampling in JAX
User: joshuaalbert
Home Page: https://jaxns.readthedocs.io/
scientific-machine-learning,🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Organization: judftteam
scientific-machine-learning,Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
Organization: juliaarrays
scientific-machine-learning,A library for Koopman Neural Operator with Pytorch.
Organization: koopman-laboratory
scientific-machine-learning,A library for scientific machine learning and physics-informed learning
User: lululxvi
Home Page: https://deepxde.readthedocs.io
scientific-machine-learning,18.S096 - Applications of Scientific Machine Learning
Organization: mitmath
scientific-machine-learning,Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Organization: sciml
Home Page: https://docs.sciml.ai/Catalyst/stable/
scientific-machine-learning,Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Organization: sciml
Home Page: https://docs.sciml.ai/DataDrivenDiffEq/stable/
scientific-machine-learning,The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Organization: sciml
scientific-machine-learning,Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqBayes/stable/
scientific-machine-learning,Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqDocs/stable/
scientific-machine-learning,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
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqFlux/stable
scientific-machine-learning,GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqGPU/stable/
scientific-machine-learning,Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqOperators/stable/
scientific-machine-learning,Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Organization: sciml
scientific-machine-learning,Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Organization: sciml
scientific-machine-learning,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.
Organization: sciml
Home Page: https://docs.sciml.ai/DiffEqDocs/stable/
scientific-machine-learning,A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Organization: sciml
Home Page: https://docs.sciml.ai/FEniCS/stable/
scientific-machine-learning,A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Organization: sciml
Home Page: https://docs.sciml.ai/Integrals/stable/
scientific-machine-learning,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)
Organization: sciml
Home Page: https://docs.sciml.ai/JumpProcesses/stable/
scientific-machine-learning,Arrays which also have a label for each element for easy scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/LabelledArrays/stable/
scientific-machine-learning,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.
Organization: sciml
Home Page: https://docs.sciml.ai/LinearSolve/stable/
scientific-machine-learning,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
Organization: sciml
Home Page: https://mtk.sciml.ai/dev/
scientific-machine-learning,A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
Organization: sciml
Home Page: https://docs.sciml.ai/NBodySimulator/stable/
scientific-machine-learning,DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Organization: sciml
Home Page: https://docs.sciml.ai/NeuralOperators/stable/
scientific-machine-learning,Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Organization: sciml
Home Page: https://docs.sciml.ai/NeuralPDE/stable/
scientific-machine-learning,High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Organization: sciml
Home Page: https://docs.sciml.ai/NonlinearSolve/stable/
scientific-machine-learning,Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.
Organization: sciml
scientific-machine-learning,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.
Organization: sciml
Home Page: https://docs.sciml.ai/Optimization/stable/
scientific-machine-learning,High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Organization: sciml
Home Page: https://diffeq.sciml.ai/latest/
scientific-machine-learning,A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.
Organization: sciml
Home Page: https://docs.sciml.ai/PolyChaos/stable/
scientific-machine-learning,Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/QuasiMonteCarlo/stable/
scientific-machine-learning,Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Organization: sciml
Home Page: https://docs.sciml.ai/RecursiveArrayTools/stable/
scientific-machine-learning,Reservoir computing utilities for scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/ReservoirComputing/stable/
scientific-machine-learning,The Base interface of the SciML ecosystem
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLBase/stable
scientific-machine-learning,Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLBenchmarksOutput/stable/
scientific-machine-learning,Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Organization: sciml
Home Page: https://book.sciml.ai/
scientific-machine-learning,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.
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLSensitivity/stable/
scientific-machine-learning,A style guide for stylish Julia developers
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLStyle/stable/
scientific-machine-learning,Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Organization: sciml
Home Page: https://tutorials.sciml.ai
scientific-machine-learning,Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Organization: sciml
scientific-machine-learning,Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
Organization: sciml
Home Page: https://diffeq.sciml.ai
scientific-machine-learning,Surrogate modeling and optimization for scientific machine learning (SciML)
Organization: sciml
Home Page: https://docs.sciml.ai/Surrogates/stable/
scientific-machine-learning,SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
Organization: sciml
Home Page: https://docs.sciml.ai/SymbolicNumericIntegration/stable/
scientific-machine-learning,Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Organization: tensordiffeq
Home Page: http://docs.tensordiffeq.io
scientific-machine-learning,A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs
Organization: ucl-bug
scientific-machine-learning,Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
User: vavrines
Home Page: https://xiaotianbai.com/Kinetic.jl/dev
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