TY - JOUR A1 - Zhang, Wei A1 - Schütte, Christof T1 - Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics T2 - Proceedings in Applied Mathematics and Mechanics N2 - High-dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning-based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning-based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples. Y1 - 2023 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9226 VL - 23 IS - 4 ER -