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pro vyhledávání: '"Rohrhofer, Franz M."'
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
Publikováno v:
Computer Physics Communications, volume 307, pages 109422, 2025, issn 0010-4655
This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental reaction-diffusion system with both simplicity and significance. The focus is on investigating Fisher's equation under conditions of large reaction
Externí odkaz:
http://arxiv.org/abs/2402.08313
Autor:
Rohrhofer, Franz M., Posch, Stefan, Gößnitzer, Clemens, García-Oliver, José M., Geiger, Bernhard C.
Flamelet models are widely used in computational fluid dynamics to simulate thermochemical processes in turbulent combustion. These models typically employ memory-expensive lookup tables that are predetermined and represent the combustion process to
Externí odkaz:
http://arxiv.org/abs/2308.01954
Approximating families of sharp solutions to Fisher's equation with physics-informed neural networks
Publikováno v:
In Computer Physics Communications February 2025 307
Publikováno v:
Transactions on Machine Learning Research, 2023(1)
This paper empirically studies commonly observed training difficulties of Physics-Informed Neural Networks (PINNs) on dynamical systems. Our results indicate that fixed points which are inherent to these systems play a key role in the optimization of
Externí odkaz:
http://arxiv.org/abs/2203.13648
Autor:
Rohrhofer, Franz M., Posch, Stefan, Gößnitzer, Clemens, García-Oliver, José M., Geiger, Bernhard C.
Publikováno v:
In Energy and AI May 2024 16
Publikováno v:
IEEE Access, vol. 11, pp. 86252-86261, 2023
Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations. Despite their recent advance, it is widely acknowledged that PINNs are diff
Externí odkaz:
http://arxiv.org/abs/2105.00862
Autor:
Rohrhofer, Franz M., Saha, Santanu, Di Cataldo, Simone, Geiger, Bernhard C., von der Linden, Wolfgang, Boeri, Lilia
Drive towards improved performance of machine learning models has led to the creation of complex features representing a database of condensed matter systems. The complex features, however, do not offer an intuitive explanation on which physical attr
Externí odkaz:
http://arxiv.org/abs/2102.00191
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Akademický článek
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Recently a new type of deep learning method has emerged, called physics-informed neural networks. Despite their success in solving problems that are governed by partial differential equations, physics-informed neural networks are often difficult to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1d90f4af6c527ed2bb17b7ab6dda9ce
http://arxiv.org/abs/2105.00862
http://arxiv.org/abs/2105.00862