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ISSN Print: 2689-3967
ISSN Online: 2689-3975
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Архив
DOI: 10.1615/JMachLearnModelComput.v2.i2
Table of Contents:
A MACHINE LEARNING APPROACH TO QUANTIFY DISSOLUTION KINETICS OF POROUS MEDIA
Huaxinyu Wang, Chenghai Li, Wei-W. Xing, Yanan Ye, Peng Wang
Huaxinyu Wang
Department of Energy Resources Engineering, Stanford University, 367
Panama Street, Stanford, CA 94305, USA
Chenghai Li
School of Mathematical Sciences, Beihang University, Beijing, China
Wei-W. Xing
School of Integrated Circuit Science and Engineering, Beihang University,
Beijing, China
Yanan Ye
School of Mathematical Sciences, Peking University, Beijing, China
Peng Wang
School of Integrated Circuit Science and Engineering, Beihang University,
Beijing, China; LMIB & Beijing Advanced Innovation Center for Big Data and Brain
Computing, Beijing, China
1-14 страниц
DOI: 10.1615/JMachLearnModelComput.2021038529
hp-VARIATIONAL PHYSICS-INFORMED NEURAL NETWORKS FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA
Mingyuan Yang, John T. Foster
Mingyuan Yang
Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas, USA
John T. Foster
Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas, USA
15-32 страниц
DOI: 10.1615/JMachLearnModelComput.2021038005
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS
Hannah Lu, Dinara Ermakova, Haruko Murakami Wainwright, Liange Zheng, Daniel M. Tartakovsky
Hannah Lu
Stanford University
Dinara Ermakova
Department of Nuclear Engineering, University of California Berkeley,
Berkeley, CA 94720, USA
Haruko Murakami Wainwright
Department of Nuclear Engineering, University of California Berkeley,
Berkeley, CA 94720, USA; Earth and Environmental Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA 94720, USA
Liange Zheng
Earth and Environmental Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA 94720, USA
Daniel M. Tartakovsky
Department of Energy Resources, Engineering, Stanford University, 367 Panama St., Stanford, CA 94305, USA
33-54 страниц
DOI: 10.1615/JMachLearnModelComput.2021038577
CONSTRAINED GAUSSIAN PROCESS REGRESSION: AN ADAPTIVE APPROACH FOR THE ESTIMATION OF HYPERPARAMETERS AND THE VERIFICATION OF CONSTRAINTS WITH HIGH PROBABILITY
Guillaume Perrin, S. Da Veiga
Guillaume Perrin
COSYS, Université Gustave Eiffel, 77420 Champs-sur-Marne, France
S. Da Veiga
Safran Tech, Safran SA, Magny-Les-Hameaux, France
55-76 страниц
DOI: 10.1615/JMachLearnModelComput.2021039837
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