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However, these processors require dedicated programming models to efficiently benefit from their massively parallel architectures.
First, we develop well-designed coarse-grained models to efficiently describe P3AT-PCBM systems.
Topographic accuracy, methods of preparation and grid size are all important for hydrodynamic models to efficiently replicate flow processes.
Rapid advances in computer and geospatial technology have made it increasingly possible to design and develop urban models to efficiently simulate spatial growth patterns.
In order to address such a problem, this work proposes a methodology, based on Kriging models, to efficiently assess the uncertainty quantification in the optimization process.
Then, 2-D flame-turbulence interactions are considered, spanning a wide range of velocity and length scale ratios, to demonstrate the ability of PC-based combustion models to efficiently capture the behavior of different combustion regimes, from flamelet-like flames to distributed reaction regimes.
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We have developed a numerical model to efficiently compute steady-state combined buoyancy and thermocapillary convection solutions.
Firstly, we develop a novel GP-based state space model to efficiently process data in a sequential manner.
Thirdly, we explore Kalman filter on this state space model to efficiently filter out the latent function values for prediction.
An approach termed rational design gives the opportunity to target regions of the parameter space by combining the knowledge gained from surface science experiments and modeling to efficiently guide HTE.
In this research, we propose a Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to integrate both high- and low-fidelity model to efficiently identify a (near) optimal design.
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