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An integrated physics-guided machine learning approach for predicting asphalt concrete fracture parameters. [PDF]
Sci RepElahi M +6 more
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A surrogate-based inverse design framework for targeted diameter control of electrospun nanofibers. [PDF]
Sci RepMahdian M, Ender F, Pardy T.
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Towards Surrogate Reaction Model Development
Volume 2: Combustion, Fuels and Emissions, Parts A and B, 2011The present paper describes the proposed strategy of fuel model design based on identification of chemical and physical criteria for the selection of initial formula of the reference fuel. The first 8 criteria established and studied in previous papers so far are combustion enthalpy, formation enthalpy, molecular weight, C/H-ratio, sooting tendency ...
Slavinskaya, Nadezhda A. +2 more
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Conservative Predictions Using Surrogate Modeling
49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference <br> 16th AIAA/ASME/AHS Adaptive Structures Conference<br> 10t, 2008[Abstract] Conservative prediction refers to calcu lations or approximations that tend to estimate safely the response of a system. The aim o f this study is to explore and compare the alternatives to produce conservative predictions wh en using surrogate models.
Picheny, Victor +3 more
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Water Resources Research, 1972
To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
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To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
openaire +1 more source
2019
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
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This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
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Surrogate Models for Coupled Microgrids
2019We consider the operation of coupled microgrids. Each microgrid consists of a number of residential energy systems, each including an energy storage device. The goal is to determine an optimal energy exchange between the microgrids, which results in a two-level optimization problem.
Grundel, S. +2 more
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