Work

Artificial Surrogate Model for Computational Fluid Dynamics

Year: 2025

Type: conference-paper

Abstract: Simulating fluid dynamics is challenging due to the computational complexity of processing high-dimensional data, which often requires significant time.Fluid behavior is typically governed by partial differential equations (PDEs), and the complexity escalates when obstacles disrupt the flow, reinforcing vorticity formation.Vorticity describes the local rotational motion of a fluid.In this paper, we present a data-driven approach to automate PDE simulations and develop surrogate models to generate fluid dynamics based on the Kármán vortex street.Our approach aims to generate accurate fluid simulations with faster computation through architectural adjustments.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-70

Authors: Abdallah Alfaham, Siegfried Mercelis

Institutions: University of Antwerp, IMEC

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480298

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