Work

Runge–Kutta random feature method for solving multiphase flow problems of cells

Year: 2025

Type: article

Abstract: Cell collective migration plays a crucial role in a variety of physiological processes. In this work, we propose the Runge–Kutta random feature method to solve the nonlinear and strongly coupled multiphase flow problems of cells, in which the random feature method in space and the explicit Runge–Kutta method in time are utilized. Experiments indicate that this algorithm can effectively deal with time-dependent partial differential equations with strong nonlinearity and achieve high accuracy both in space and time. Moreover, in order to improve the computational efficiency and save computational resources, we choose to implement parallelization and non-automatic differentiation strategies in our simulations. We also provide error estimates for the Runge–Kutta random feature method, and a series of numerical experiments are shown to validate our method.

Source: Physics of Fluids

DOI: 10.1063/5.0252273

Authors: Yangtao Deng, Qiaolin He

Institution: Sichuan University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G5473515595, G6890557519