Recent developments in Machine Learning approaches for modelling physical
systems have begun to mirror the past development of numerical methods in the
computational sciences. In this survey, we begin by providing an example of
this with the parallels between the development trajectories of graph neural
network acceleration for physical simulations and particle-based approaches. We
then give an overview of simulation approaches, which have not yet found their
way into state-of-the-art Machine Learning methods and hold the potential to
make Machine Learning approaches more accurate and more efficient. We conclude
by presenting an outlook on the potential of these approaches for making
Machine Learning models for science more efficient.

本次调查论述了机器学习在物理系统建模中的发展趋势，重点介绍了图神经网络加速和基于粒子的方法的发展轨迹，并探讨了一些未被应用于现阶段机器学习方法的模拟方法，这些方法有可能使机器学习方法更准确，更高效。最后，论文展望了这些方法对于科学的机器学习模型提高效率的潜力。