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Conditional physics informed neural networks

  • Published:2022-01 Issue: Volume:104 Page:106041
  • ISSN:1007-5704
  • Container-title:Communications in Nonlinear Science and Numerical Simulation
  • language:en
  • Short-container-title:Communications in Nonlinear Science and Numerical Simulation

Author:

Kovacs Alexander,Exl Lukas,Kornell Alexander,Fischbacher JohannORCID,Hovorka Markus,Gusenbauer Markus,Breth LeoniORCID,Oezelt Harald,Yano Masao,Sakuma Noritsugu,Kinoshita Akihito,Shoji Tetsuya,Kato Akira,Schrefl ThomasORCID

Funder

Austrian Science Fund

Christian Doppler Forschungsgesellschaft

Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung

Bundesministerium für Digitalisierung und Wirtschaftsstandort

Publisher

Elsevier BV

Subject

Applied Mathematics,Modeling and Simulation,Numerical Analysis

Reference49 articles.

1. Deep learning for magnetic field estimation;Khan;IEEE Trans Magn,2019

2. Deep fluids: A generative network for parameterized fluid simulations;Kim;Comput Graph Forum,2019

3. Learning magnetization dynamics;Kovacs;J Magn Magn Mater,2019

4. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J Comput Phys,2019

5. Pydens: A python framework for solving differential equations with neural networks;Koryagin,2019

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