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of 29
pro vyhledávání: '"Gößnitzer, Clemens"'
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
The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low emission levels. Due to the wide range of design and operating parame
Externí odkaz:
http://arxiv.org/abs/2308.01743
Autor:
Klawitter, Marc, Wüthrich, Silas, Cartier, Patrick, Albrecht, Patrick, Herrmann, Kai, Gößnitzer, Clemens, Pirker, Gerhard, Wimmer, Andreas
Publikováno v:
In Fuel 1 November 2024 375
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
Akademický článek
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Autor:
Posch, Stefan1 (AUTHOR) clemens.goessnitzer@lec.tugraz.at, Gößnitzer, Clemens1 (AUTHOR) gerhard.pirker@lec.tugraz.at, Ofner, Andreas B.2 (AUTHOR) aofner@know-center.at, Pirker, Gerhard1 (AUTHOR) andreas.wimmer@lec.tugraz.at, Wimmer, Andreas1,3 (AUTHOR)
Publikováno v:
Energies (19961073). Apr2022, Vol. 15 Issue 7, p2325-N.PAG. 16p.