Learning Based Multi Continuum Model

Learning-based multi-continuum models aim to improve the accuracy and efficiency of simulations across various scales, particularly in complex systems where traditional single-continuum approaches fall short. Current research focuses on integrating neural networks with established continuum mechanics equations, often employing techniques like dual-porosity models or Gaussian process representations to capture multiscale interactions and implicit shapes. These models show promise in diverse applications, including physical property estimation from visual data, multiscale fluid flow simulations, and even the analysis of complex biological systems like protein-lipid interactions, offering a powerful tool for bridging the gap between microscopic detail and macroscopic behavior.