Automated transport separation using the neural shifted proper orthogonal decomposition

Autor:	Zorawski, Beata, Burela, Shubhaditya, Krah, Philipp, Marmin, Arthur, Schneider, Kai
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Mathematics - Numerical Analysis Physics - Computational Physics Physics - Fluid Dynamics
Druh dokumentu:	Working Paper
Popis:	This paper presents a neural network-based methodology for the decomposition of transport-dominated fields using the shifted proper orthogonal decomposition (sPOD). Classical sPOD methods typically require an a priori knowledge of the transport operators to determine the co-moving fields. However, in many real-life problems, such knowledge is difficult or even impossible to obtain, limiting the applicability and benefits of the sPOD. To address this issue, our approach estimates both the transport and co-moving fields simultaneously using neural networks. This is achieved by training two sub-networks dedicated to learning the transports and the co-moving fields, respectively. Applications to synthetic data and a wildland fire model illustrate the capabilities and efficiency of this neural sPOD approach, demonstrating its ability to separate the different fields effectively. Comment: Proceedings not peer-reviewed yet. Code available: https://github.com/MOR-transport/automated_NsPOD
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.17539 Zobrazit plný text záznamu View this record from Arxiv