Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
DOI10.1142/s0218202522500452zbMath1512.92094arXiv2206.12625WikidataQ115523476 ScholiaQ115523476MaRDI QIDQ6157162
Xueyu Zhu, Giulia Bertaglia, Lorenzo Pareschi, Chuan Lu
Publication date: 19 June 2023
Published in: Mathematical Models and Methods in Applied Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.12625
diffusion limitphysics-informed neural networksasymptotic-preserving methodsepidemic compartmental modelsdiscrete-velocity transport modelsmultiscale hyperbolic models
Epidemiology (92D30) Artificial neural networks and deep learning (68T07) Reaction-diffusion equations (35K57) Numerical methods for stiff equations (65L04) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08) Initial-boundary value problems for first-order hyperbolic equations (35L04)
Related Items (3)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A reaction-diffusion model for a deterministic diffusive epidemic
- A generalization of the Kermack-McKendrick deterministic epidemic model
- Diffusive limit for finite velocity Boltzmann kinetic models
- Spatial spread of epidemic diseases in geographical settings: seasonal influenza epidemics in Puerto Rico
- Simulating the spread of COVID-19 \textit{via} a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion
- Control with uncertain data of socially structured compartmental epidemic models
- Kinetic models for epidemic dynamics with social heterogeneity
- Multiscale models of COVID-19 with mutations and variants
- Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
- Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty
- Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- Solving multiscale steady radiative transfer equation using neural networks with uniform stability
- Analysis of a reaction-diffusion cholera epidemic model in a spatially heterogeneous environment
- Propagation of epidemics along lines with fast diffusion
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The Mathematics of Infectious Diseases
- The diffusive limit for Carleman-type kinetic models
- Particle, kinetic, and hydrodynamic models of swarming
- The Diffusion Limit of Transport Equations in Biology
- The nonlinear diffusion limit for generalized Carleman models: the initial-boundary value problem
- Uniformly Accurate Diffusive Relaxation Schemes for Multiscale Transport Equations
- A multiscale view of nonlinear diffusion in biology: From cells to tissues
- The Dawning of a New Era in Applied Mathematics
- Hyperbolic models for the spread of epidemics on networks: kinetic description and numerical methods
- Hyperbolic compartmental models for epidemic spread on networks with uncertain data: Application to the emergence of COVID-19 in Italy
- Kinetic Modelling of Epidemic Dynamics: Social Contacts, Control with Uncertain Data, and Multiscale Spatial Dynamics
- A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world
- Modeling and simulating the spatial spread of an epidemic through multiscale kinetic transport equations
- Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
- Asymptotic-preserving schemes for multiscale physical problems
This page was built for publication: Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
