Our colleague Sibo Cheng has led a fantastic and very thorough review on the new but very hot topic of the development and leveraging of machine learning (ML) techniques for modelling unstructured grid data in computational physics. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. The review places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. Emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field.

The paper is entitled Machine learning for modelling unstructured grid data in computational physics: A review, is published in Information Fusion.