The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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Updated
May 25, 2024 - Julia
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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
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
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
Code for running the analyses from the article "Propofol destabilizies neural dynamics across cortex"
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.
Some slides, code and videos about R, mostly for mathematical modellers
Source code generator for differential equation solvers.
Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Thompson and Shampine's DDE_SOLVER, a Fortran library for delay differential equations.
Neural Laplace Control for Continuous-time Delayed Systems - an offline RL method combining Neural Laplace dynamics model and MPC planner to achieve near-expert policy performance in environments with irregular time intervals and an unknown constant delay.
A sample DDE with random delay. The system is from C. Letellier et al:
Python package for solving Differential Equations with Discrete and Distributed delays
Julia package for solving Differential Equations with Discrete and Distributed delays
Matlab package for solving Differential Equations with Discrete and Distributed delays
Differential equation models that incorporate waning immunity
This is the repository of the codes, data and plots used in the project. https://www.mdpi.com/1099-4300/23/3/288
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