Physical Domain Reconstruction with Finite Volume Neural Networks-Reference-Cited by-同舟云学术

Physical Domain Reconstruction with Finite Volume Neural Networks

Published:2023-04-29 Issue:1 Volume:37 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Horuz Coşku Can¹,Karlbauer Matthias¹,Praditia Timothy²,Butz Martin V.¹,Oladyshkin Sergey²,Nowak Wolfgang²,Otte Sebastian¹

Affiliation:

1. Neuro-Cognitive Modeling, Department of Computer Science and Department of Psychology, Faculty of Science, University of Tübingen, Tübingen, Germany

2. Department of Stochastic Simulation and Safety Research for Hydrosystems, University of Stuttgart, Stuttgart, Germany

Funder

Alexander von Humboldt-Stiftung

Deutsche Forschungsgemeinschaft

Deutscher Akademischer Austauschdienst

International Max Planck Research School for Intelligent Systems

Publisher

Informa UK Limited

Subject

Artificial Intelligence

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2023.2204261

Reference23 articles.

1. Battaglia, P. W., J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.

2. Learning, planning, and control in a monolithic neural event inference architecture

3. Infering Boundary Conditions in Finite Volume Neural Networks

4. Physics-informed machine learning

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