Stochastic parameter reduced-order model based on hybrid machine learning approaches

arXiv:2403.17032v1 Announce Type: new
Abstract: Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena makes it extremely challenging to develop full-order models (FOMs) and apply them to studying many quantities of interest. In contrast, appropriate reduced-order models (ROMs) are favored due to their high computational efficiency and ability to describe the key dynamics and …

abstract arxiv complexity complex systems cs.lg however hybrid machine machine learning natural nature prediction state stochastic systems type understanding