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pro vyhledávání: '"Franz, M"'
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental representation of a reaction-diffusion system with both simplicity and significance. The focus lies specifically in investigating Fisher's equation
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
Autor:
Strassmeier, K. G., Weber, M., Gruner, D., Ilyin, I., Steffen, M., Baratella, M., Järvinen, S., Granzer, T., Barnes, S. A., Carroll, T. A., Mallonn, M., Sablowski, D., Gabor, P., Brown, D., Corbally, C., Franz, M.
Publikováno v:
A&A 671, A7 (2023)
We embarked on a high-resolution optical spectroscopic survey of bright Transiting Exoplanet Survey Satellite (TESS) stars around the Northern Ecliptic Pole (NEP), dubbed the Vatican-Potsdam-NEP (VPNEP) survey. Our NEP coverage comprises 1067 stars,
Externí odkaz:
http://arxiv.org/abs/2302.01794
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