Архив

Том 2, 2021 / Выпуск 2

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 78712, USA

John T. Foster
Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas 78712, 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
MIT

Dinara Ermakova
Department of Nuclear Engineering, University of California Berkeley, Berkeley, CA 94720, USA

Haruko Murakami Wainwright
Department of Nuclear Science & Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 24-107, Cambridge, MA 02139, USA

Liange Zheng
Earth and Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Daniel M. Tartakovsky
Department of Energy Science & Engineering, 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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