Abstract: We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) …

In this talk, we discuss the paper "Next generation reservoir computing", by Gauthier, et.al, Nat. Comm., 2021. Abstract : Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. …

In this talk, we discuss the paper "SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection" by Yueyue Yao, et.al., Neural Networks, 2024.  Abstract  Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel …

In this talk, we study and discuss the paper "Laplacian renormalization group for heterogeneous networks" by Pablo Villegas et.al, Nature Physics, 2023. Abstract  The renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its …

In this talk, we discuss the paper "cellFlow: a generative flow-based model for single-cell count data" by A. Palma et.al, ICLR, 2024. Abstract Generative modeling for single-cell RNA-seq has proven transformative in crucial fields such as learning single-cell representations and perturbation responses. However, despite their appeal in relevant applications involving data augmentation and unseen cell …