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      NN-EUCLID: Deep-learning hyperelasticity without stress data

      Author(s): , , , , ,
      Publication date Created:
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      Journal: Journal of the Mechanics and Physics of Solids
      Publisher: Elsevier BV

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          Multilayer feedforward networks are universal approximators

          Kurt Hornik, Maxwell Stinchcombe, Halbert L. White (1989)
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            Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations

            M. Raissi, P Perdikaris, G.E. Karniadakis (2019)
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              Dropout: a simple way to prevent neural networks from overfitting

              N SRIVASTAVA, S. Nitish, H. GEOFFREY … (2014)
              Article 0 comments Cited 511 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Prakash Thakolkaran: (View ORCID Profile)
                Akshay Joshi: (View ORCID Profile)
                Yiwen Zheng: (View ORCID Profile)
                Moritz Flaschel: (View ORCID Profile)
                Laura De Lorenzis: (View ORCID Profile)
                Siddhant Kumar: (View ORCID Profile)
                Journal
                Title: Journal of the Mechanics and Physics of Solids
                Abbreviated Title: Journal of the Mechanics and Physics of Solids
                Publisher: Elsevier BV
                ISSN (Print): 00225096
                Publication date Created: December 2022
                Publication date (Print): December 2022
                Volume: 169
                Page: 105076
                Article
                DOI: 10.1016/j.jmps.2022.105076
                SO-VID: 9fe1228a-1963-4ac3-8cff-592470ab5e0c
                Copyright © © 2022
                License:

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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