Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Minartz, Koen"'
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward so
Externí odkaz:
http://arxiv.org/abs/2405.17260
Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science and metallurgy. These processes are highly nonlinear, stochastic, and se
Externí odkaz:
http://arxiv.org/abs/2405.16608
Publikováno v:
Nucl. Fusion 63 126012 (2023)
Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applica
Externí odkaz:
http://arxiv.org/abs/2305.18944
Neural networks are emerging as a tool for scalable data-driven simulation of high-dimensional dynamical systems, especially in settings where numerical methods are infeasible or computationally expensive. Notably, it has been shown that incorporatin
Externí odkaz:
http://arxiv.org/abs/2305.14286
Simulators driven by deep learning are gaining popularity as a tool for efficiently emulating accurate but expensive numerical simulators. Successful applications of such neural simulators can be found in the domains of physics, chemistry, and struct
Externí odkaz:
http://arxiv.org/abs/2210.01123
Publikováno v:
VLDB Journal International Journal on Very Large Data Bases; Mar2024, Vol. 33 Issue 2, p481-505, 25p
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.