Model discovery of compartmental models with graph-supported neural networks
From MaRDI portal
Publication:6090296
DOI10.1016/j.amc.2023.128392OpenAlexW4387762980MaRDI QIDQ6090296
Hiram Calvo, Fernando Javier Aguilar-Canto, Carlos Brito-Loeza
Publication date: 14 November 2023
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2023.128392
ordinary differential equationsneural network approximationcompartmental modelsmodel discoverygraph-supported neural networks
Artificial intelligence (68Txx) Inference from stochastic processes (62Mxx) Qualitative theory for ordinary differential equations (34Cxx)
Cites Work
- Unnamed Item
- Deep neural networks motivated by partial differential equations
- Global stability for SIRS epidemic models with general incidence rate and transfer from infectious to susceptible
- Global dynamics in a Beddington-DeAngelis prey-predator model with density dependent death rate of predator
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- GINNs: graph-informed neural networks for multiscale physics
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Solving many-electron Schrödinger equation using deep neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Bayesian physics informed neural networks for real-world nonlinear dynamical systems
- Automated reverse engineering of nonlinear dynamical systems
- Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability
- Approximation by superpositions of a sigmoidal function
- Characterization of a two-parameter mixed-mode electrochemical behavior regime using neural networks
This page was built for publication: Model discovery of compartmental models with graph-supported neural networks
