Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session S53: Machine Learning
8:00 AM–10:36 AM,
Thursday, March 9, 2023
Room: Room 307
Sponsoring
Units:
GDS DFD DMP
Chair: Jennifer Hobbs, Zurich North America
Abstract: S53.00001 : Towards learning a Lattice Boltzmann collisional operator
8:00 AM–8:12 AM
Presenter:
Alessandro Gabbana
(Eindhoven University of Technology)
Authors:
Alessandro Gabbana
(Eindhoven University of Technology)
Alessandro Corbetta
(Eindhoven University of Technology)
Vitaliy Gyrya
(Los Alamos National Laboratory)
Daniel Livescu
(LANL)
Joost Prins
(Eindhoven University of Technology)
Federico Toschi
(Eindhoven University of Technology)
We compare the accuracy achieved in the simulation of a few selected benchmarks, employing several approaches for the architecture of the neural network. We show that only by embedding in the neural network physics properties, such as conservation laws and symmetries, it is possible to correctly reproduce the time dynamic of simple fluid flows.
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