Integrating prior epidemiological knowledge embedded within mechanistic models with the
data-mining capabilities of artificial intelligence (AI) offers transformative potential for …

This paper presents a critical analysis of the literature and perspective research ideas for
modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic …

In this work, we aim to formalize a novel scientific machine learning framework to reconstruct
the hidden dynamics of the transmission rate, whose inaccurate extrapolation can …

In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep
Operator Networks (APCONs) designed to solve the multiscale time-dependent linear …

A physics-informed neural network (PINN) embedded with the susceptible–infected–
removed (SIR) model is devised to understand the temporal evolution dynamics of infectious …

In this paper, we present two novel Asymptotic-Preserving Neural Networks (APNNs) for
tackling multiscale time-dependent kinetic problems, encompassing the linear transport …

This paper deals with the micro–macro derivation of models from the underlying description
provided by methods of the kinetic theory for active particles. We consider the so-called …

Excerpt This book focuses on IMEX methods, with particular emphasis on their application to
systems of PDEs. IMEX methods have proven to be highly effective for solving a wide range …

Compartmental models provide simple and efficient tools to analyze the relevant
transmission processes during an outbreak, to produce short-term forecasts or transmission …

We propose a two-scale neural network method for solving partial differential equations
(PDEs) with small parameters using physics-informed neural networks (PINNs). We directly …