Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equation...
#计算机科学#Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
翻译 - 具有GPU支持和高效灵敏度分析的可区分SDE求解器。
#计算机科学#Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
翻译 - JAX 中的数值微分方程求解器。可自微分且支持 GPU。
#计算机科学#A package for the sparse identification of nonlinear dynamical systems from data
#计算机科学#A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
#计算机科学#A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
#计算机科学#Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
#计算机科学#Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
翻译 - 代码“不规则时间序列的神经控制微分方程”
A Control Systems Toolbox for Julia
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Python package for solving partial differential equations using finite differences.
#计算机科学#Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
#计算机科学#A Python Package For System Identification Using NARMAX Models
#计算机科学#Arrays with arbitrarily nested named components.
#计算机科学#Code for the paper "Learning Differential Equations that are Easy to Solve"
Nonlinear Dynamics: A concise introduction interlaced with code
Neural Graph Differential Equations (Neural GDEs)
Computing reachable states of dynamical systems in Julia