197 citations · 271 across the 15 of their papers we have counts for
13 papers · 1 filter
Asymptotically stable polarization of multi-agent gradient flows over manifolds
La Mi, Jorge Gonçalves, Johan Markdahl
Multi-agent systems are known to exhibit stable emergent behaviors, including polarization, over or highly symmetric nonlinear spaces. In this article, we eschew lin…
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems
Zhexuan Zeng, Zuogong Yue, Alexandre Mauroy +2
Low sampling frequency challenges the exact identification of the continuous-time (CT) dynamical system from sampled data, even when its model is identifiable. The necessary and su…
A Full Bayesian Approach to Sparse Network Inference using Heterogeneous Datasets
Junyang Jin, Ye Yuan, Jorge Goncalves
Network inference has been attracting increasing attention in several fields, notably systems biology, control engineering and biomedicine. To develop a therapy, it is essential to…
High Precision Variational Bayesian Inference of Sparse Linear Networks
Junyang Jin, Ye Yuan, Jorge Goncalves
Sparse networks can be found in a wide range of applications, such as biological and communication networks. Inference of such networks from data has been receiving considerable at…
Data-driven Discovery of Cyber-Physical Systems
Ye Yuan, Xiuchuan Tang, Wei Pan +5
Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical…
Distributed Kalman filtering with minimum-time consensus algorithm
Ye Yuan, Ling Shi, Jun Liu +3
Fueled by applications in sensor networks, these years have witnessed a surge of interest in distributed estimation and filtering. A new approach is hereby proposed for the Distrib…
On Identification of Sparse Multivariable ARX Model: A Sparse Bayesian Learning Approach
J. Jin, Y. Yuan, W. Pan +4
This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structur…
Identifying Biochemical Reaction Networks From Heterogeneous Datasets
Wei Pan, Ye Yuan, Lennart Ljung +2
In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain…
On minimal realisations of dynamical structure functions
Ye Yuan, Keith Glover, Jorge Goncalvees
Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships be…
Network Reconstruction from Intrinsic Noise
David Hayden, Ye Yuan, Jorge Goncalves
This paper considers the problem of inferring an unknown network of dynamical systems driven by unknown, intrinsic, noise inputs. Equivalently we seek to identify direct causal dep…
Minimal realization of the dynamical structure function and its application to network reconstruction
Ye Yuan, Guy-Bart Stan, Sean Warnick +1
Network reconstruction, i.e., obtaining network structure from data, is a central theme in systems biology, economics and engineering. In some previous work, we introduced dynamica…
Reconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach
Wei Pan, Ye Yuan, Guy-Bart Stan
Reconstruction of biochemical reaction networks is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Diffe…
The Meaning of Structure in Interconnected Dynamic Systems
E. Yeung, J. Goncalves, H. Sandberg +1
Interconnected dynamic systems are a pervasive component of our modern infrastructures. The complexity of such systems can be staggering, which motivates simplified representations…