Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data-Reference-Cited by-同舟云学术

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
language:
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Author:

Kashi Aditya¹^ORCID

Affiliation:

1. National Center for Computational Sciences, Oak Ridge National Laboratory, United States of America

Funder

US Department of Energy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3626088

Reference13 articles.

1. Daniel Arndt , Wolfgang Bangerth , Maximilian Bergbauer , Marco Feder , Marc Fehling , Johannes Heinz , Timo Heister , Luca Heltai , Martin Kronbichler , Matthias Maier , Peter Munch , Jean-Paul Pelteret , Bruno Turcksin , David Wells , and Stefano Zampini . 2023 , accepted for publication. The deal . II Library , Version 9.5. Journal of Numerical Mathematics ( 2023 , accepted for publication). https://dealii.org/deal95-preprint.pdf Daniel Arndt, Wolfgang Bangerth, Maximilian Bergbauer, Marco Feder, Marc Fehling, Johannes Heinz, Timo Heister, Luca Heltai, Martin Kronbichler, Matthias Maier, Peter Munch, Jean-Paul Pelteret, Bruno Turcksin, David Wells, and Stefano Zampini. 2023, accepted for publication. The deal.II Library, Version 9.5. Journal of Numerical Mathematics (2023, accepted for publication). https://dealii.org/deal95-preprint.pdf

2. Machine Learning for Fluid Mechanics

3. Physics-informed neural networks for inverse problems in nano-optics and metamaterials

4. Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

5. Viktor Grimm Alexander Heinlein and Axel Klawonn. 2023. Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks. arxiv:2308.02137 [math.NA] Viktor Grimm Alexander Heinlein and Axel Klawonn. 2023. Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks. arxiv:2308.02137 [math.NA]