Machine Learning Methods for Multiscale Physics and Urban Engineering Problems
Somya Sharma,
Marten Thompson,
Debra Laefer,
Michael Lawler,
Kevin McIlhany,
Olivier Pauluis,
Dallas R. Trinkle,
Snigdhansu Chatterjee
Affiliations
Somya Sharma
Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street SE, Minneapolis, MN 55455, USA
Marten Thompson
School of Statistics, University of Minnesota-Twin Cities, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, USA
Debra Laefer
Department of Civil and Urban Engineering, New York University, Rogers Hall RH 411, Brooklyn, NY 11201, USA
Michael Lawler
Department of Physics, Applied Physics and Astronomy, Binghamton University, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Kevin McIlhany
Physics Department, United States Naval Academy, 572 Holloway Rd. m/s 9c, Annapolis, MD 21402, USA
Olivier Pauluis
Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
Dallas R. Trinkle
Department of Materials Science & Engineering, University of Illinois, 201 Materials Science and Engineering Building, 1304 W. Green St. MC 246, Urbana, IL 61801, USA
Snigdhansu Chatterjee
School of Statistics, University of Minnesota-Twin Cities, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, USA
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.
