Sharma Somya, Thompson Marten, Laefer Debra, Lawler Michael, McIlhany Kevin, Pauluis Olivier, Trinkle Dallas R, Chatterjee Snigdhansu
Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street SE, Minneapolis, MN 55455, USA.
School of Statistics, University of Minnesota-Twin Cities, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, USA.
Entropy (Basel). 2022 Aug 16;24(8):1134. doi: 10.3390/e24081134.
We present an overview of four challenging research areas in multiscale physics and engineering as well as four data science topics that may be developed for addressing these challenges. We focus on multiscale spatiotemporal problems in light of the importance of understanding the accompanying scientific processes and engineering ideas, where "multiscale" refers to concurrent, non-trivial and coupled models over scales separated by orders of magnitude in either space, time, energy, momenta, or any other relevant parameter. Specifically, we consider problems where the data may be obtained at various resolutions; analyzing such data and constructing coupled models led to open research questions in various applications of data science. Numeric studies are reported for one of the data science techniques discussed here for illustration, namely, on approximate Bayesian computations.
我们概述了多尺度物理与工程中四个具有挑战性的研究领域，以及为应对这些挑战可能会发展起来的四个数据科学主题。鉴于理解伴随的科学过程和工程理念的重要性，我们专注于多尺度时空问题，其中“多尺度”是指在空间、时间、能量、动量或任何其他相关参数上，跨越数量级分离的尺度上的并发、非平凡且耦合的模型。具体而言，我们考虑的数据可能是在各种分辨率下获取的问题；分析此类数据并构建耦合模型在数据科学的各种应用中引发了开放性研究问题。本文报道了对这里讨论的一种数据科学技术的数值研究，即近似贝叶斯计算，用于说明目的。
