Evidence map›Paper›PMID 36010800›Full record

ArticleEntropy (Basel, Switzerland)2022

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Somya SharmaDepartment of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street SE, Minneapolis, MN 55455, USA.ORCID 0000-0003-2194-162X
Marten ThompsonSchool of Statistics, University of Minnesota-Twin Cities, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, USA.ORCID 0000-0002-0012-7664
Debra LaeferDepartment of Civil and Urban Engineering, New York University, Rogers Hall RH 411, Brooklyn, NY 11201, USA.
Michael LawlerDepartment of Physics, Applied Physics and Astronomy, Binghamton University, 4400 Vestal Parkway East, Binghamton, NY 13902, USA.ORCID 0000-0002-2319-2274
Kevin McIlhanyPhysics Department, United States Naval Academy, 572 Holloway Rd. m/s 9c, Annapolis, MD 21402, USA.ORCID 0000-0002-3906-8999
Olivier PauluisCourant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.
Dallas R TrinkleDepartment of Materials Science & Engineering, University of Illinois, 201 Materials Science and Engineering Building, 1304 W. Green St. MC 246, Urbana, IL 61801, USA.
Snigdhansu ChatterjeeSchool of Statistics, University of Minnesota-Twin Cities, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455, USA.ORCID 0000-0002-7986-0470

Funding

National Science Foundation 1737918, 1939916,1939956,1940145,1940260,1940287
6 · The paper itself

Abstract

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.

Indexed as

approximate Bayesian computationapproximate Hamiltoniandimension reductionhybrid approachmoist atmosphere dynamicsmolecular dynamicsmulti-resolution Gaussian Processspin icetime evolutionurban engineering

Identifiers

PMID36010800
PMCPMC9407195

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.