PlumX Metrics
Embed PlumX Metrics

A comparison of neural-network architectures to accelerate high-order h/p solvers

Physics of Fluids, ISSN: 1089-7666, Vol: 36, Issue: 10
2024
  • 0
    Citations
  • 0
    Usage
  • 4
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    4
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Universidad Politecnica de Madrid Researcher Reports Recent Findings in Fluids Physics (A comparison of neural-network architectures to accelerate high-order h/p solvers)

2024 OCT 30 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Researchers detail new data in fluids physics. According to

Article Description

High-order solvers are accurate but computationally expensive as they require small time steps to advance the solution in time. In this work, we include a corrective forcing to a low-order solution to achieve high accuracy while advancing in time with larger time steps and achieving fast computations. This work is a continuation of our previous research [Manrique de Lara and Ferrer, “Accelerating high order discontinuous Galerkin solvers using neural networks: 1D Burgers' equation,” Comput. Fluids 235, 105274 (2022) and F. Manrique de Lara and E. Ferrer, “Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations,” J. Comput. Phys. 489, 112253 (2023).], where we compare advanced neural networks: convolutional neural network (CNN) and long short-term memory (LSTM) networks to obtain the corrective forcing that corrects the low-order solution. The CNN exploits local spatial correlations while the LSTM accounts for temporal dependencies in the flow, expanding the validity of the low-order solution. Experimental results on the Taylor-Green vortex problem at Re = 1600, which includes laminar, transitional, and turbulent regimes, demonstrate significant accelerations of these advanced networks over the fully connected network.

Bibliographic Details

Oscar A. Marino; Adrian Juanicotena; Jon Errasti; David Mayoral; Fernando Manrique de Lara; Esteban Ferrer; Ricardo Vinuesa

AIP Publishing

Engineering; Physics and Astronomy; Chemical Engineering

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know