Архив

Том 3, 2022 / Выпуск 1

DOI: 10.1615/JMachLearnModelComput.v3.i1

Table of Contents:

MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE Ari L. Frankel, Cosmin Safta, Coleman Alleman, Reese E. Jones

Ari L. Frankel
Sandia National Laboratories, Livermore, California 94551, USA

Cosmin Safta
P.O.Box 969, MS 9051; Computational Science and Analysis, Sandia National Laboratories, Livermore, CA, 94551

Coleman Alleman
Sandia National Laboratories, Livermore, California 94551, USA

Reese E. Jones
Sandia National Laboratories, Livermore, California 94551, USA

1-30 страниц
DOI: 10.1615/JMachLearnModelComput.2021039688

TRANSFER LEARNING ON MULTIFIDELITY DATA Dong H. Song, Daniel M. Tartakovsky

Dong H. Song
Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA

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

31-47 страниц
DOI: 10.1615/JMachLearnModelComput.2021038925

A NEURAL SOLVER FOR VARIATIONAL PROBLEMS ON CAD GEOMETRIES WITH APPLICATION TO ELECTRIC MACHINE SIMULATION Moritz von Tresckow, Stefan Kurz, Herbert De Gersem, Dimitrios Loukrezis

Moritz von Tresckow
Technische Universität Darmstadt, Institute for Accelerator Science and Electromagnetic Fields, Schlossgartenstr. 8, 64289 Darmstadt, Germany

Stefan Kurz
University of Jyväskylä, Faculty of Information Technology, Seminaarinkatu 15, Jyväskylä, Finland

Herbert De Gersem
Institute for Accelerator Science and Electromagnetic Fields (TEMF), Technische Universität Darmstadt, Schlossgartenstraße 8, 64289 Darmstadt, Germany; Centre for Computational Engineering, Technische Universität Darmstadt, Dolivostraße 15, 64293 Darmstadt, Germany

Dimitrios Loukrezis
Institute for Accelerator Science and Electromagnetic Fields, Technische Universität Darmstadt, Darmstadt, Germany; Centre for Computational Engineering, Technische Universität Darmstadt, Darmstadt, Germany

49-75 страниц
DOI: 10.1615/JMachLearnModelComput.2022041753

LEVERAGING LOCAL VARIATION IN DATA: SAMPLING AND WEIGHTING SCHEMES FOR SUPERVISED DEEP LEARNING Paul Novello, G. Poëtte, D. Lugato, P. M. Congedo

Paul Novello
CESTA, CEA, Le Barp, 33114, France; CMAP, Ecole Polytechnique, 91120, Palaiseau, France; Platon, Inria Paris Saclay, 91120, Palaiseau, France

G. Poëtte
CESTA, CEA, Le Barp, 33114, France

D. Lugato
CESTA, CEA, Le Barp, 33114, France

P. M. Congedo
CMAP, Ecole Polytechnique, 91120, Palaiseau, France; Platon, Inria Paris Saclay, 91120, Palaiseau, France

77-105 страниц
DOI: 10.1615/JMachLearnModelComput.2022041819

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