Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning-Reference-Cited by-同舟云学术

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Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning

Published:2022 Issue:23 Volume:55 Page:1-6
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Anantharaman Ranjan,Abdelrehim Anas,Jain Anand,Pal Avik,Sharp Danny,Utkarsh ,Edelman Alan,Rackauckas Chris

Publisher

Elsevier BV

Subject

General Medicine

Reference32 articles.

1. Efficient generation and selection of virtual populations in quantitative systems pharmacology models;Allen;CPT: pharmacometrics & systems pharmacology,2016

2. Accelerating simulation of stiff nonlinear systems using continuous-time echo state networks;Anantharaman,2021

3. Composable and Reusable Neural Surrogates to Predict System Response of Causal Model Components;Anantharaman,2021

4. Bergen et al., 2022, https://sites.brown.edu/bergen-lab/research/what-is-sciml/

5. BioModels database: a repository of mathematical models of biological processes;Chelliah,2013

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1. Broad fractional-order echo state network with slime mould algorithm for multivariate time series prediction;Applied Soft Computing;2024-09

2. Investigating the Surrogate Modeling Capabilities of Continuous Time Echo State Networks;Mathematical and Computational Applications;2024-01-24

3. Physics-informed neural networks for the reaction-diffusion Brusselator model;Mathematical Modeling and Computing;2024

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