Journal article
Multi-resolution physics-aware recurrent convolutional neural network for complex flows
APL machine learning, Vol.3(4), 046110
12/01/2025
DOI: 10.1063/5.0295883
Abstract
We present MRPARCv2, Multi-resolution Physics-Aware Recurrent Convolutional Neural Network, designed to model complex flows by embedding the structure of advection-diffusion-reaction equations and leveraging a multi-resolution architecture. MRPARCv2 introduces hierarchical discretization and cross-resolution feature communication to improve the accuracy and efficiency of flow simulations. We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository and demonstrate significant improvements when compared to the single resolution baseline model, in both Variance Scaled Root Mean Squared Error and physics-driven metrics, including turbulent kinetic energy spectra and mass-temperature distributions. Despite having 30% fewer trainable parameters, MRPARCv2 outperforms its predecessor by up to 50% in roll-out prediction error and 86% in spectral error. A preliminary study on uncertainty quantification was performed, and we also analyzed the model's performance under different levels of abstractions of the flow, specifically on sampling subsets of field variables. We find that the absence of physical constraints on the equation of state (EOS) in the network architecture leads to degraded accuracy. A variable substitution experiment confirms that this issue persists regardless of which physical quantity is predicted directly. Our findings highlight the advantages of multi-resolution inductive bias for capturing multi-scale flow dynamics and suggest the need for future PIML models to embed EOS knowledge to enhance physical fidelity.(c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/). https://doi.org/10.1063/5.0295883
Details
- Title: Subtitle
- Multi-resolution physics-aware recurrent convolutional neural network for complex flows
- Creators
- Xinlun Cheng - University of VirginiaJoseph Choi - University of VirginiaH. S. Udaykumar - Univ Iowa, Dept Mech Engn, 103 S Capitol St, Iowa City, IA 52242 USAStephen Baek - University of Virginia
- Resource Type
- Journal article
- Publication Details
- APL machine learning, Vol.3(4), 046110
- DOI
- 10.1063/5.0295883
- ISSN
- 2770-9019
- eISSN
- 2770-9019
- Publisher
- AIP Publishing
- Number of pages
- 17
- Language
- English
- Date published
- 12/01/2025
- Academic Unit
- Engineering Administration; Injury Prevention Research Center; Chemical and Biochemical Engineering; Mechanical Engineering
- Record Identifier
- 9985091814802771
Metrics
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