Batch sequential design of optimal experiments for improved predictive maturity in physics-based modeling

Atamturktur, Sez; Williams, Brian J.; Egeberg, Matthew C.; Unal, Cetin

doi:10.1007/s00158-013-0915-8

Cited by 13 publications

(10 citation statements)

References 35 publications

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“…In BSD, for a given set of initial experiments and a numerical model, a batch of new 'optimal' experiments are selected, where the size of the batch as well as the number of batches are defined by the available resources. This work is presented in Report #2 [Atamturktur, S., Williams, B., Edgeberg, M., Unal, C. (2013) "Batch Sequential Design of Optimal Experiments for Improved Predictive Maturity in Physics-based Modeling," Structural and Multidisciplinary Optimization,Vol. 48,No.…”

Section: Figure 1 Framework For Improving Predictive Maturity Of Mulmentioning

confidence: 99%

Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

Atamturktur

Unal

Hemez

et al. 2015

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Section: Figure 1 Framework For Improving Predictive Maturity Of Mulmentioning

confidence: 99%

Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

Atamturktur

Unal

Hemez

et al. 2015

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“…As the available experimental data increase, the modeler's ability to continually improve prediction uncertainty is expected to rise with diminishing returns, and the empirical representation of prediction bias is expected to converge to a consistent and systematic level (Hemez et al 2010;Atamturktur et al 2011). However, the reduction in prediction uncertainty and the convergence of the prediction bias both exhibit path dependency, i.e., the rates at which the prediction bias converges and prediction uncertainty reduces depend on (1) the control settings at which the experiments are conducted within the operational domain, and (2) the sequence in which they are conducted (Atamturktur et al 2013). Therefore, the proper selection of experimental settings is vitally important for the efficient use of experimental resources in model validation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the proper selection of experimental settings is vitally important for the efficient use of experimental resources in model validation. Only recently, however, have techniques, taken from the optimal design of computer experiments developed for efficient construction of response surface models, been investigated as a means of exploiting this path dependency in the design of physical experiments (Jiang and Mahadevan 2006;Williams et al 2011;Atamturktur et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The BSD methodology can be used to select the settings of future experiments in an iterative manner, building on the information gained from previously conducted experiments. Earlier implementations of BSD aimed to optimize a predefined criterion to maximize the stability of the inferred prediction bias (Williams et al 2011;Atamturktur et al 2013). Here, stability implies that the addition of new experiments to the validation data set has a minimal influence on the empirically trained discrepancy bias, ensuring a proper identification of the model's fundamental inability to reproduce the experimental measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The C-EIPS criterion explores the entirety of the operational domain by considering the effect of coverage, and it exploits areas within the domain that exhibit high variability in the empirically trained discrepancy bias emulator. Deployed on a multivariate problem focusing on the viscoplastic self-consistent (VPSC) code for modeling the plasticity of metals (Lebensohn and Tomé 1993), the performance of C-EIPS was measured against two optimization criteria as an effective means of selecting validation experiments (Atamturktur et al 2013). This comparison was completed using a quantitative and objective metric, the Predictive Maturity Index (PMI), originally proposed by Hemez et al (2010) and later modified by Stull et al (2011).…”

Section: Introductionmentioning

confidence: 99%

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Selection Criterion Based on an Exploration-Exploitation Approach for Optimal Design of Experiments

Atamturktur

Hegenderfer²,

Williams

et al. 2015

J. Eng. Mech.

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Modeling and simulation are being relied upon in many fields of science and engineering as computational surrogates for experimental testing. To justify the use of these simulations for decision making, however, it is critical to determine, and when necessary mitigate, the biases and uncertainties in model predictions, a task that invariably requires validation experiments. To use experimental resources efficiently, validation experiments must be designed to achieve the maximum possible increases in model predictive ability through the calibration of the model against experiments. This need for efficiency is addressed by the concept of optimally designing validation experiments, which constitutes optimizing a predefined criterion while selecting the settings of experiments. This paper presents an improved optimization criterion that incorporates two important factors for the optimal design of validation experiments: (1) how well the model reproduces the validation experiments, and (2) how well the validation experiments cover the domain of applicability. The criterion presented herein selects the appropriate settings for future experiments with the goal of achieving a desired level of predictive ability in the computer model through the use of a minimal number of validation experiments. The criterion explores the entirety of the application domain by including the effect of coverage, and exploits areas of the domain with high variability by including the effect of empirically defined discrepancy bias. The effectiveness of this new criterion is compared with two well-established criteria through a simulated case study involving the stress-strain response and textural evolution of polycrystalline materials. The proposed criterion is demonstrated as efficient at improving the predictive capabilities of the numerical model, particularly when the amount of experimental data available for validation is low.

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