Work

Surrogate Model Using A U-NET-ConvLSTM Architecture For A Macroscopic Pedestrian Flow Model

Year: 2025

Type: conference-paper

Abstract: The aim of this paper is to demonstrate whether a deep neural network can replace a FEM simulation of a complex system and capture its non-linear dependencies on geometry. The system we are interested in is the macroscopic pedestrian flow in evacuation scenarios, modelled by two coupled partially differential equations (PDE) on 2D domains, possibly with holes, with mixed boundary conditions. Based on a literature review, we narrow down promising network architectures and study them with varying input variables. We develop a surrogate model based on a U-Net-ConvLSTM network. Our tests show the basic ability of such models to mimic the simulation, although further tests with larger training sets are needed.

Authors: Doris Bohnet, Yvonne Wehrlein

Institution: HTWG Hochschule Konstanz - Technik, Wirtschaft und Gestaltung

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Funder: Interreg

Award: G5769187499

OpenAlex ID: W4412596240