64 Open source projects that are alternatives of or similar to HelicopterSciML.jl

Latent Differential Equations models in Julia.

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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

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The SciML Scientific Machine Learning Software Organization Website

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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)

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)

Fortran Library for numerical INTegration of differential equations

Fortran Object-Oriented Differential-equations Integration Environment, FOODIE

C++ library for ODE integration via Taylor's method and LLVM

A brian2 extension to simulate spiking neural networks on GPUs

Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.

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R package for statistical inference using partially observed Markov processes

library of numerical methods using Armadillo

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Computational Methods for Numerical Analysis

Numerical methods implementation in Python.

Python library for ODE integration via Taylor's method and LLVM

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Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.

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IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

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Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

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