Zobrazeno 1 - 10
of 4 086
pro vyhledávání: '"Franz, M. A."'
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
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
Autor:
Anastasia G. Ilgen, Eric Borguet, Franz M. Geiger, Julianne M. Gibbs, Vicki H. Grassian, Young-Shin Jun, Nadine Kabengi, James D. Kubicki
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to o
Externí odkaz:
https://doaj.org/article/eedb92c0f62846b3a8c04da8572c8a8b
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:
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, José M. García-Oliver, Bernhard C. Geiger
Publikováno v:
Energy and AI, Vol 16, Iss , Pp 100341- (2024)
Artificial Neural Networks (ANNs) have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics. Complex reaction mechanisms, however, present a challenge for standard ANN approaches
Externí odkaz:
https://doaj.org/article/1fb8c1fd661b45d5a15612ab39f193bd
Autor:
Rohrhofer, Franz M., Posch, Stefan, Gößnitzer, Clemens, García-Oliver, José M., Geiger, Bernhard C.
Publikováno v:
In Energy and AI May 2024 16
We employ general arguments and numerical simulations to show that unpaired Majorana zero modes can occur in cores of Abrikosov vortices at the interface between a three-dimensional topological insulator, such as Bi$_2$Se$_3$, and a twisted bilayer o
Externí odkaz:
http://arxiv.org/abs/2106.12473
Autor:
Ma, Emily, Kim, Jeongmin, Chang, HanByul, Ohno, Paul E., Jodts, Richard J., Miller III, Thomas F., Geiger, Franz M.
Second harmonic generation amplitude and phase measurements are acquired in real time from fused silica:water interfaces that are subjected to ionic strength transitions conducted at pH 5.8. In conjunction with atomistic modeling, we identify correla
Externí odkaz:
http://arxiv.org/abs/2106.02893