Mathematical research of stochastic processes with time delays
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Updated
Aug 12, 2020 - Jupyter Notebook
Mathematical research of stochastic processes with time delays
Inoue, K. et al. Oscillation dynamics underlie functional switching of NF-κB for B-cell activation. npj Syst. Biol. Appl. 2, 16024 (2016).
This is the repository of the codes, data and plots used in the project. https://www.mdpi.com/1099-4300/23/3/288
Differential equation models that incorporate waning immunity
Matlab package for solving Differential Equations with Discrete and Distributed delays
Julia package for solving Differential Equations with Discrete and Distributed delays
Python package for solving Differential Equations with Discrete and Distributed delays
A sample DDE with random delay. The system is from C. Letellier et al:
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.
Thompson and Shampine's DDE_SOLVER, a Fortran library for delay differential equations.
Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Some slides, code and videos about R, mostly for mathematical modellers
COCO constructors for delay-differential-algebraic equations
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.
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
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.
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
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