Data driven modeling for self-similar dynamics

arXiv:2310.08282v3 Announce Type: replace
Abstract: Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-similarity is prevalent in complex systems, hinting that large-scale complex systems can be modeled at a reduced cost. In this paper, we introduce a multiscale neural network framework that incorporates self-similarity as prior knowledge, facilitating the modeling of self-similar dynamical systems. For deterministic dynamics, …

abstract arxiv challenges complex systems cond-mat.stat-mech cost cs.lg data data-driven dynamics modeling scale systems type understanding