A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data

TL;DR

This paper introduces a physics-informed GraphGPS framework for reconstructing 3D Riemann problems from sparse data, improving accuracy and efficiency in capturing shocks and discontinuities in fluid flows.

Contribution

The work develops a novel GraphGPS-based method that incorporates physics-awareness and shock-sensitive message passing for improved 3D flow reconstruction.

Findings

Outperforms existing machine learning benchmarks.

Achieves sharper shock and discontinuity reconstructions.

Reduces computational cost with modified message-passing.

Abstract

In compressible fluid flow, reconstructing shocks, discontinuities, rarefactions, and their interactions from sparse measurements is an important inverse problem with practical applications. Moreover, physics-informed machine learning has recently become an increasingly popular approach for performing reconstructions tasks. In this work we explore a machine learning recipe, known as GraphGPS, for reconstructing canonical compressible flows known as 3D Riemann problems from sparse observations, in a physics-informed manner. The GraphGPS framework combines the benefits of positional encodings, local message-passing of graphs, and global contextual awareness, and we explore the latter two components through an ablation study. Furthermore, we modify the aggregation step of message-passing such that it is aware of shocks and discontinuities, resulting in sharper reconstructions of these features. Additionally, we modify message-passing such that information flows strictly from known nodes only, which results in computational savings, better training convergence, and no degradation of reconstruction accuracy. We also show that the GraphGPS framework outperforms numerous machine learning benchmarks.