Operator learning for predicting multiscale bubble growth dynamics
نویسندگان
چکیده
Simulating and predicting multiscale problems that couple multiple physics dynamics across many orders of spatiotemporal scales is a great challenge has not been investigated systematically by deep neural networks (DNNs). Herein, we develop framework based on operator regression, the so-called network (DeepONet), with long-term objective to simplify modeling avoiding fragile time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions phenomena. To this end, as first step, investigate if DeepONet can learn different scale regimes, one at deterministic macroscale other stochastic microscale regime inherent thermal fluctuations. Specifically, test effectiveness accuracy in multirate bubble growth dynamics, which described Rayleigh–Plesset (R–P) equation modeled nucleation cavitation process dissipative particle (DPD). First, generate data using R–P caused randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show properly trained DeepONets accurately predict outperform long short-term memory networks. We also demonstrate extrapolate outside input distribution only very few new measurements. Subsequently, train DPD corresponding dynamics. Although are noisy collect sparse points trajectories, model able mean GRF pressures. Taken together, our findings be employed unify models problem, hence providing insight into role regression via DNNs tackling realistic simplifying descriptions.
منابع مشابه
Online Machine Learning Techniques for Predicting Operator Performance
Electrical Engineering and Computer Science Institute of Software Engineering and Theoretical Computer Science
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ژورنال
عنوان ژورنال: Journal of Chemical Physics
سال: 2021
ISSN: ['1520-9032', '1089-7690', '0021-9606']
DOI: https://doi.org/10.1063/5.0041203