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pro vyhledávání: '"Rohrhofer A"'
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:
Aoki, Sinya, Aoki, Yasumichi, Fukaya, Hidenori, Hashimoto, Shoji, Kanamori, Issaku, Kaneko, Takashi, Nakamura, Yoshifumi, Rohrhofer, Christian, Suzuki, Kei, Ward, David
We study the $U(1)_A$ anomaly at high temperatures of $N_f=2+1$ lattice QCD with chiral fermions. Gauge ensembles are generated with M\"obius domain-wall (MDW) fermions, and the measurements are reweighted to those with overlap fermions. We report on
Externí odkaz:
http://arxiv.org/abs/2401.14022
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:
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:
Aoki, Sinya, Aoki, Yasumichi, Fukaya, Hidenori, Hashimoto, Shoji, Kanamori, Issaku, Kaneko, Takashi, Nakamura, Yoshifumi, Rohrhofer, Christian, Suzuki, Kei
We study the $U(1)_A$ anomaly in the high-temperature phase of $N_f=2+1$ lattice QCD with chiral fermions. Gauge ensembles are generated with M\"obius domain-wall (MDW) fermions, and in the measurements the determinant is reweighted to that of overla
Externí odkaz:
http://arxiv.org/abs/2203.16059
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:
JLQCD collaboration, Aoki, S., Aoki, Y., Fukaya, H., Hashimoto, S., Rohrhofer, C., Suzuki, K.
Publikováno v:
PoS(LATTICE2021)050
In the early days of QCD, the axial $U(1)$ anomaly was considered as a trigger for the breaking of the $SU(2)_L\times SU(2)_R$ symmetry through topological excitations of gluon fields. However, it has been a challenge for lattice QCD to quantify the
Externí odkaz:
http://arxiv.org/abs/2111.02048
The temperature of the chiral restoration phase transition at 130 MeV as well as the temperature of the center symmetry ("deconfinement") phase transition in a pure glue theory at 300 MeV are two independent temperatures and their interplay determine
Externí odkaz:
http://arxiv.org/abs/2108.08073
Autor:
Rohrhofer, Franz M., Posch, Stefan, Gößnitzer, Clemens, García-Oliver, José M., Geiger, Bernhard C.
Publikováno v:
In Energy and AI May 2024 16