PAPM: A Physics-aware Proxy Model for Process Systems

TL;DR

This paper introduces PAPM, a physics-aware proxy model that integrates partial physics knowledge and a temporal-spatial module to improve generalization and efficiency in process system modeling.

Contribution

PAPM fully incorporates partial prior physics and a temporal-spatial module, enhancing generalization and reducing computational costs compared to existing models.

Findings

Achieves 6.7% performance improvement over state-of-the-art models.

Requires only 1% of the parameters of previous methods.

Demonstrates superior generalization across five benchmark tasks.

Abstract

In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities. As a promising alternative, physics-aware models incorporate partial physics knowledge to ameliorate these challenges. Although demonstrating efficacy, they fall short in terms of exploration depth and universality. To address these shortcomings, we introduce a physics-aware proxy model (PAPM) that fully incorporates partial prior physics of process systems, which includes multiple input conditions and the general form of conservation relations, resulting in better out-of-sample generalization. Additionally, PAPM contains a holistic temporal-spatial stepping module for flexible adaptation across various process systems. Through systematic comparisons with state-of-the-art pure data-driven and physics-aware models across five two-dimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6.7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method. The code is available at https://github.com/pengwei07/PAPM.