Stochastic differential equations (SDEs) are mathematical models that are widely used to
describe complex processes or phenomena perturbed by random noise from different sources. …

Multivariate time series anomaly detection with missing data is one of the most pending
issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven …

High-modulus materials that yield small elastic deformation under mechanical loads hold
great promise for use in a wide range of engineering applications. However, the discovery and …

Maintenance of machinery equipment in smart manufacturing requires real-time health
monitoring, strongly supported by the rapid evolution of Artificial Intelligence (AI) technologies. …

Multivariate time series anomaly detection is one of the most indispensable yet troublesome
links in complex industrial processes. The main challenge lies in discovering the …

This paper presents a “structured” learning approach for the identification of continuous partial
differential equation (PDE) models with both constant and spatial-varying coefficients. The …

Traditional machine learning relies on a centralized data pipeline for model training in various
applications; however, data are inherently fragmented. Such a decentralized nature of …

This paper addresses reconstruction of linear dynamic networks from heterogeneous
datasets. Those datasets consist of measurements from linear dynamical systems in multiple …

Performing multiple experiments is common when learning internal mechanisms of complex
systems. These experiments can include perturbations of parameters or external …

Dynamical structure functions (DSFs) provide means for modelling networked dynamical
systems and exploring interactive structures thereof. There have been several studies on …