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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.
This paper presents the development of a velocity propagation method (VPM) based dynamic compact zonal model to efficiently describe the airflow and temperature patterns in a data center with a contained cold aisle.
These results can guide future decisions on the degree of complexity that is warranted in a physically-based rainfall runoff model to efficiently and explicitly estimate time invariant subsurface pathlines and transit time distribution.
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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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.
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