scholarly journals Bayesian optimal experimental design for parameter estimation and response predictions in complex dynamical systems

2017 ◽  
Vol 199 ◽  
pp. 972-977 ◽  
Author(s):  
Costas Papadimitriou ◽  
Costas Argyris
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Experimental Design ◽  
Optimal Experimental Design ◽  
Complex Dynamical Systems

Optimal experimental design for the parameter estimation of conduction heated foods

2001 ◽  
Vol 48 (2) ◽  
pp. 109-119 ◽  
Author(s):  
H.B. Nahor ◽  
N. Scheerlinck ◽  
R. Verniest ◽  
J. De Baerdemaeker ◽  
B.M. Nicolaı̈
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Optimal Experimental Design

scholarly journals Using Modified Intelligent Experimental Design in Parameter Estimation of Chaotic Systems

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zahra Shourgashti ◽  
Hamid Keshvari ◽  
Shirin Panahi
Keyword(s):  
Parameter Estimation ◽  
System Identification ◽  
Dynamical Systems ◽  
Experimental Design ◽  
Chaotic Systems ◽  
Initial Condition ◽  
Sampled Data ◽  
One Dimensional ◽  
Using Data ◽  
Set Up

Computational modeling plays an important role in prediction and optimization of real systems and processes. Models usually have some parameters which should be set up to the proper value. Therefore, parameter estimation is known as an important part of the modeling and system identification. It usually refers to the process of using sampled data to estimate the optimum values of parameters. The accuracy of model can be increased by adjusting its parameters to the optimum value which need a richer dataset. One simple solution for having a richer dataset is increasing the amount of data, but that can be costly and time consuming. When using data from animals or people, it is especially important to have a proper plan. There are several available methods for parameter estimation in dynamical systems; however there are some basic differences in chaotic systems due to their sensitivity to initial condition (butterfly effect). Accordingly, in this paper, a new cost function which is proper for chaotic systems is applied to the chaotic one-dimensional map. Then the efficiency of a newly introduced intelligent method experimental design in extracting proper data is investigated. The results show the success of the proposed method.

Predicting Optimal Borehole Locations for Parameter Estimation in Geothermal Reservoirs Using Optimal Experimental Design

2021 ◽  
Author(s):  
Johanna Fink ◽  
Ralf Seidler
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Mathematical Optimization ◽  
Simulation Software ◽  
Geothermal Reservoir ◽  
Temperature Measurements ◽  
Optimal Experimental Design ◽  
Reservoir Engineering ◽  
Hydraulic Permeability ◽  
Geothermal Reservoirs

<p>Drilling boreholes during exploration and development of geothermal reservoirs not only involves high cost, but also bears significant risks of failure. In geothermal reservoir engineering, techniques of optimal experimental design (OED) have the potential to improve the decision making process. Previous publications explained the formulation and implementation of this mathematical optimization problem and demonstrated its feasibility for finding borehole locations in two- and three-dimensional reservoir models that minimize the uncertainty of estimating hydraulic permeability of a model unit from temperature measurements. Subsequently, minimizing the uncertainty of the parameter estimation results in a more reliable parametrization of the reservoir simulation, improving the overall process in geothermal reservoir engineering.</p><p>Various OED techniques are implemented in the Environment for Combining Optimization and Simulation Software (EFCOSS). To address problems arising from geothermal modeling, this software framework links mathematical optimization software with SHEMAT-Suite, a geothermal simulation code for fluid flow and heat transport through porous media. This contribution shows how to determine experimental conditions such that the uncertainty when estimating different parameters of model units from temperature measurements in the borehole is minimized. Numerical simulations of synthetic geothermal reservoir scenarios are presented to demonstrate the OED workflow and its applicability to geothermal reservoir modeling</p>

Optimal experimental design for parameter estimation in unstructured growth models

1994 ◽  
Vol 10 (5) ◽  
pp. 480-488 ◽  
Author(s):  
M. Baltes ◽  
R. Schneider ◽  
C. Sturm ◽  
M. Reuss
Keyword(s):  
Parameter Estimation ◽  
Experimental Design ◽  
Growth Models ◽  
Optimal Experimental Design
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