A Region-Based History Matching Approach Using Data-Driven Modeling Techniques

2016 ◽  
Author(s):  
M. Mital ◽  
A. Verde ◽  
L. Saputelli
Keyword(s):  
History Matching ◽  
Data Driven ◽  
Modeling Techniques ◽  
Using Data ◽  
Data Driven Modeling

Data-Driven Modeling Techniques to Estimate Dispersion Relations of Structural Components

2018 ◽  
Author(s):  
Vijaya V. N. Sriram Malladi ◽  
Mohammad I. Albakri ◽  
Pablo A. Tarazaga ◽  
Serkan Gugercin
Keyword(s):  
Frequency Response ◽  
Dispersion Relations ◽  
Data Driven ◽  
Response Functions ◽  
Structural Components ◽  
Frequency Range ◽  
Frequency Response Functions ◽  
Material Inhomogeneity ◽  
Modeling Techniques ◽  
Data Driven Modeling

Dispersion relations describe the frequency-dependent nature of elastic waves propagating in structures. Experimental determination of dispersion relations of structural components, such as the floor of a building, can be a tedious task, due to material inhomogeneity, complex boundary conditions, and the physical dimensions of the structure under test. In this work, data-driven modeling techniques are utilized to reconstruct dispersion relations over a predetermined frequency range. The feasibility of this approach is demonstrated on a one-dimensional beam where an exact solution of the dispersion relations is attainable. Frequency response functions of the beam are obtained numerically over the frequency range of 0–50kHz. Data-driven dynamical model, constructed by the vector fitting approach, is then deployed to develop a state-space model based on the simulated frequency response functions at 16 locations along the beam. This model is then utilized to construct dispersion relations of the structure through a series of numerical simulations. The techniques discussed in this paper are especially beneficial to such scenarios where it is neither possible to find analytical solutions to wave equations, nor it is feasible to measure dispersion curves experimentally. In the present work, actual experimental data is left for future work, but the complete framework is presented here.

scholarly journals Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology

2009 ◽  
Vol 6 (6) ◽  
pp. 7055-7093 ◽  
Author(s):  
A. Elshorbagy ◽  
G. Corzo ◽  
S. Srinivasulu ◽  
D. P. Solomatine
Keyword(s):  
Polynomial Regression ◽  
Predictive Accuracy ◽  
Lower Layer ◽  
Data Driven ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Evolutionary Polynomial Regression ◽  
Modeling Techniques ◽  
Modeling Experiment ◽  
Data Driven Modeling

Abstract. A comprehensive data driven modeling experiment is presented in two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. Multiple linear regression and naïve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed for the modeling experiment. Twelve different realizations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modeling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both predictive accuracy and uncertainty of the modeling techniques can be evaluated. The implementation of the modeling techniques, results and analysis, and the findings of the modeling experiment are deferred to the second part of this paper.

Data-driven modeling techniques for indoor CO2 estimation

2017 ◽  
Author(s):  
Bob Vergauwen ◽  
Oscar Mauricio Agudelo ◽  
Raj Thilak Rajan ◽  
Frank Pasveer ◽  
Bart De Moor
Keyword(s):  
Data Driven ◽  
Modeling Techniques ◽  
Data Driven Modeling

Data-driven Crowd Modeling Techniques: A Survey

2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Author(s):  
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai
Keyword(s):  
Game Design ◽  
State Of The Art ◽  
Crowd Simulation ◽  
Data Driven ◽  
Future Research ◽  
Crowd Modeling ◽  
The Past ◽  
Modeling Techniques ◽  
Comprehensive Survey ◽  
Data Driven Modeling

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

scholarly journals A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

2018 ◽  
Vol 3 (2) ◽  
pp. 245-263
Author(s):  
Franco van Wyk ◽  
Anahita Khojandi ◽  
Brian Williams ◽  
Don MacMillan ◽  
Robert L. Davis ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Data Driven ◽  
Physiological Data ◽  
Benefit Analysis ◽  
Data Acquisition Systems ◽  
Using Data ◽  
Data Driven Modeling
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