• Corpus ID: 235359136

Machine learning based approach to fluid dynamics

@inproceedings{Taradiy2021MachineLB,
  title={Machine learning based approach to fluid dynamics},
  author={Kirill Taradiy and Kai Zhou and Jan Steinheimer and R. V. Poberezhnyuk and Volodymyr Vovchenko and Horst Stoecker},
  year={2021}
}
Kirill Taradiy, 2 Kai Zhou, Jan Steinheimer, Roman V. Poberezhnyuk, 1 Volodymyr Vovchenko, and Horst Stoecker 5, 6 Frankfurt Institute for Advanced Studies, Giersch Science Center, D-60438 Frankfurt am Main, Germany Xidian-FIAS International Joint Research Center, Giersch Science Center, D-60438 Frankfurt am Main, Germany Bogolyubov Institute for Theoretical Physics, 03680 Kyiv, Ukraine Nuclear Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720… 

References

SHOWING 1-10 OF 42 REFERENCES

Riemann Solvers and Numerical Methods for Fluid Dynamics

Note: Includes references and index Reference Record created on 2004-09-07, modified on 2016-08-08

Smoothed Particle Hydrodynamics: A Meshfree Particle Method

SPH Concept and Essential Formulation Constructing Smoothing Functions SPH for General Dynamic Fluid Flows Discontinuous SPH (DSPH) SPH for Simulating Explosions SPH for Underwater Explosion Shock

Reviews of modern physics 61

  • 75
  • 1989

Computer Graphics forum

Gpu gems 2: programming techniques for high-performance graphics and general-purpose computation

This sequel to the best-selling, first volume of GPU Gems details the latest programming techniques for today's graphics processing units (GPUs).

SPECIAL FUNCTIONS

___________________ ___________________ ___________________ ___________________ ___________________ ___________________ ___________ ___________ ___________________ ___________ ___________________

Int

  • J. Mod. Phys. A 28, 1340011
  • 2013

Rept

  • Prog. Phys. 80, 096901
  • 2017