期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 188, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.commatsci.2020.110178
关键词
Surrogate model; Gaussian process; Optimal time allocation; Uncertainty; Molecular dynamics simulations; Glass-forming system
资金
- Army Research Laboratory [W911NF-122-0023]
The paper presents a numerical optimization framework based on Gaussian processes for optimal time allocation over simulations at different locations, to construct a surrogate model with uncertainty estimation to approximate the full simulation.
Simulation models have been utilized in a wide range of real-world applications for behavior predictions of complex physical systems or material designs of large structures. While extensive simulation is mathematically preferable, external limitations such as available resources are often necessary considerations. With a fixed computational resource (i.e., total simulation time), we propose a Gaussian process-based numerical optimization framework for optimal time allocation over simulations at different locations, so that a surrogate model with uncertainty estimation can be constructed to approximate the full simulation. The proposed framework is demonstrated first via two synthetic problems, and later using a real test case of a glass-forming system with divergent dynamic relaxations where a Gaussian process is constructed to estimate the diffusivity and its uncertainty with respect to the temperature.
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