scholarly journals State-Space Reconstruction and Spatio-Temporal Prediction of Lattice Dynamical Systems

2007 ◽  
Vol 52 (4) ◽  
pp. 622-632 ◽  
Author(s):  
Lingzhong Guo ◽  
Stephen A Billings
Keyword(s):  
Dynamical Systems ◽  
State Space ◽  
State Space Reconstruction ◽  
Temporal Prediction ◽  
Lattice Dynamical Systems ◽  
Spatio Temporal

Online Data-Driven Prediction of Spatio-Temporal System Behavior Using High-Fidelity Simulations and Sparse Sensor Measurements

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
X. Zhao ◽  
S. Azarm ◽  
B. Balachandran
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Measurement Data ◽  
Data Driven ◽  
High Fidelity ◽  
Simulation Data ◽  
Online Data ◽  
Temporal Prediction ◽  
Spatio Temporal

Abstract Predicting the behavior or response for complicated dynamical systems during their operation may require high-fidelity and computationally costly simulations. Because of the high computational cost, such simulations are generally done offline. The offline simulation data can then be combined with sensors measurement data for online, operational prediction of the system's behavior. In this paper, a generic online data-driven approach is proposed for the prediction of spatio-temporal behavior of dynamical systems using their simulation data combined with sparse, noisy sensors measurement data. The approach relies on an offline–online approach and is based on an integration of dimension reduction, surrogate modeling, and data assimilation techniques. A step-by-step application of the proposed approach is demonstrated by a simple numerical example. The performance of the approach is also evaluated by a case study which involves predicting aeroelastic response of a joined-wing aircraft in which sensors are sparsely placed on its wing. Through this case study, it is shown that the results obtained from the proposed spatio-temporal prediction technique have comparable accuracy to those from the high-fidelity simulation, while at the same time significant reduction in computational expense is achieved. It is also shown that, for the case study, the proposed approach has a prediction accuracy that is relatively robust to the sensors’ locations.

Attractors for multi-valued lattice dynamical systems with nonlinear diffusion terms

2021 ◽  
Author(s):  
Panpan Zhang ◽  
Anhui Gu
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Diffusion ◽  
Upper Semicontinuity ◽  
Growth Conditions ◽  
Pullback Attractor ◽  
Random Lattice ◽  
Lipschitz Continuous ◽  
Lattice Dynamical Systems ◽  
Long Term Behavior

This paper is devoted to the long-term behavior of nonautonomous random lattice dynamical systems with nonlinear diffusion terms. The nonlinear drift and diffusion terms are not expected to be Lipschitz continuous but satisfy the continuity and growth conditions. We first prove the existence of solutions, and establish the existence of a multi-valued nonautonomous cocycle. We then show the existence and uniqueness of pullback attractors parameterized by sample parameters. Finally, we establish the measurability of this pullback attractor by the method based on the weak upper semicontinuity of the solutions.

Spatio-Temporal Prediction of Roadside PM2.5 based on Sparse Mobile Sensing and Traffic Information

2021 ◽  
Author(s):  
A. Kakarla ◽  
V. S. K. R. Munagala ◽  
T. Ishizaka ◽  
A. Fukuda ◽  
S. Jana
Keyword(s):  
Mobile Sensing ◽  
Traffic Information ◽  
Temporal Prediction ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document