Архив

Том 3, 2022 / Выпуск 2

SPECIAL ISSUE:MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN SUSTAINABILITY RESEARCH
GUEST EDITORS: VALENTINA CIRIELLO& DANIEL M. TARTAKOVSKY

DOI: 10.1615/JMachLearnModelComput.v3.i2

Table of Contents:

MACHINE LEARNING TECHNIQUES FOR APPLICATIONS IN SUSTAINABILITY RESEARCH Valentina Ciriello, Daniel M. Tartakovsky

Valentina Ciriello
University of Bologna

Daniel M. Tartakovsky
Department of Energy Resources, Engineering, Stanford University, 367 Panama St., Stanford, CA 94305, USA

v-vi страниц
DOI: 10.1615/JMachLearnModelComput.2022045267

IMPUTATION OF CONTIGUOUS GAPS AND EXTREMES OF SUBHOURLY GROUNDWATER TIME SERIES USING RANDOM FORESTS Dipankar Dwivedi, Utkarsh Mital, Boris Faybishenko, Baptiste Dafflon, Charuleka Varadharajan, Deborah Agarwal, Kenneth H. Williams, Carl I. Steefel, Susan S. Hubbard

Dipankar Dwivedi
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Utkarsh Mital
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Boris Faybishenko
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Baptiste Dafflon
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Charuleka Varadharajan
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Deborah Agarwal
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Kenneth H. Williams
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Carl I. Steefel
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Susan S. Hubbard
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

1-22 страниц
DOI: 10.1615/JMachLearnModelComput.2021038774

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky

Ramakrishna Tipireddy
Pacific Northwest National Laboratory, MSIN K7-90, Richland, Washington 99352, USA

Paris Perdikaris
Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

Panos Stinis
Pacific Northwest National Laboratory, MSIN K7-90, Richland, Washington 99352, USA

Alexandre M. Tartakovsky
Pacific Northwest National Laboratory, MSIN K7-90, Richland, Washington 99352, USA; Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA

23-46 страниц
DOI: 10.1615/JMachLearnModelComput.2022041787

INVERSE ANALYSIS WITH VARIATIONAL AUTOENCODERS: A COMPARISON OF SHALLOW AND DEEP NETWORKS Hao Wu, Daniel O'Malley, John K. Golden, Velimir V. Vesselinov

Hao Wu
Computational Earth Science, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

Daniel O'Malley
Computational Earth Science, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

John K. Golden
Information Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

Velimir V. Vesselinov
Computational Earth Science, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

47-70 страниц
DOI: 10.1615/JMachLearnModelComput.2022042093

GAUSSIAN PROCESS REGRESSION AND CONDITIONAL KARHUNEN-LOÈVE EXPANSION FOR FORWARD UNCERTAINTY QUANTIFICATION AND INVERSE MODELING IN THE PRESENCE OF MEASUREMENT NOISE Jing Li, Alexandre M. Tartakovsky

Jing Li
Pacific Northwest National Laboratory, Richland, Washington 99354, USA

71-86 страниц
DOI: 10.1615/JMachLearnModelComput.2022041893

ELECTRIC LOAD AND POWER FORECASTING USING ENSEMBLE GAUSSIAN PROCESS REGRESSION T. Ma, David A. Barajas-Solano, R. Huang, Alexandre M. Tartakovsky

T. Ma
Pacific Northwest National Laboratory, Richland, Washington 99354, USA

David A. Barajas-Solano
Pacific Northwest National Laboratory, Richland, Washington 99354, USA

R. Huang
Pacific Northwest National Laboratory, Richland, Washington 99354, USA

87-110 страниц
DOI: 10.1615/JMachLearnModelComput.2022041871

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