Work

Interpretable surrogate modeling of extrusion flows with deep learning

Year: 2025

Type: article

Source: Physics of Fluids

DOI: 10.1063/5.0298999

Authors: Daniel Stoecklein, Ghulam Destgeer, Caleb Munger, Yulin Zhou, Dino Di Carlo

Institutions: Rose–Hulman Institute of Technology, Technical University of Munich, University of California, Los Angeles

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4417370182

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Cites: 59

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