Dynamical System Models in the Life Sciences and Their Underlying Scientific Issues

10.1142/10124 ◽  
2017 ◽  
Author(s):  
Frederic Y M Wan
Keyword(s):  
Dynamical System ◽  
Life Sciences ◽  
System Models ◽  
Dynamical System Models

Networked Dynamical System Models

2012 ◽  
pp. 21-27
Author(s):  
Ziyang Meng ◽  
Tao Yang ◽  
Karl H. Johansson
Keyword(s):  
Dynamical System ◽  
System Models ◽  
Dynamical System Models

The Dynamics of Embedded-Charge Microenergy Harvesting

2010 ◽  
Vol 5 (2) ◽  
Author(s):  
Carl V. Lutzer ◽  
David S. Ross
Keyword(s):  
Dynamical System ◽  
Mechanical Energy ◽  
High Density ◽  
System Models ◽  
Novel Technology ◽  
Microelectronic Devices ◽  
Dynamical System Models

The microelectronic devices that are ubiquitous these days are limited by the need for batteries. Various methods of harvesting ambient mechanical energy have been proposed and are being developed. Recently, Potter has developed a method for embedding charge, at high density, stably at the interfaces of dissimilar insulators. In this paper, we present and analyze dynamical system models that Potter and co-workers have used to optimize microenergy harvesters based on this novel technology.

Discrepancy Prediction in Dynamical System Models Under Untested Input Histories

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Kyle Neal ◽  
Zhen Hu ◽  
Sankaran Mahadevan ◽  
Jon Zumberge
Keyword(s):  
Dynamical System ◽  
Simulation Model ◽  
Model Prediction ◽  
Surrogate Model ◽  
Surrogate Modeling ◽  
Model Bias ◽  
Time Step ◽  
System Models ◽  
Input Time ◽  
Dynamical System Models

This paper presents a probabilistic framework for discrepancy prediction in dynamical system models under untested input time histories, based on information gained from validation experiments. Two surrogate modeling-based methods, namely observation surrogate and bias surrogate, are developed to predict the bias of a dynamical system simulation model under untested input time history. In the first method, a surrogate model is built for the observed experimental output, and the model bias for the untested input is obtained by comparing the output of the observation surrogate with the output of the physics-based model. The second method constructs a surrogate model for the bias in terms of the inputs in the conducted experiments. The bias surrogate model is then used to correct the simulation model prediction at each time-step under a predictor–corrector scheme to predict the model bias under untested conditions. A neural network-based surrogate modeling technique is employed to implement the proposed methodology. The bias prediction result is reported in a probabilistic manner, in order to account for the uncertainty of the surrogate model prediction. An air cycle machine case study is used to demonstrate the effectiveness of the proposed bias prediction framework.

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