Towards real-time fluid dynamics simulation: a data-driven NN-MPS method and its implementation

DOI10.1080/13873954.2023.2184835zbMath1530.76054OpenAlexW4323314981MaRDI QIDQ6161765

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Publication date: 27 June 2023

Published in: Mathematical and Computer Modelling of Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/13873954.2023.2184835

zbMATH Keywords

Navier-Stokes equationspressure Poisson equationedge computingmachine-learning algorithmdam collapse problemfully connected artificial neural networkLagrangian moving particle semi-implicit method

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Navier-Stokes equations for incompressible viscous fluids (76D05) Particle methods and lattice-gas methods (76M28) Basic methods in fluid mechanics (76M99)

Cites Work

• Unnamed Item
• Step-by-step improvement of MPS method in simulating violent free-surface motions and impact-loads
• New insights into error accumulation due to biased particle distribution in semi-implicit particle methods
• A general neural particle method for hydrodynamics modeling
• Computational haemodynamics of small vessels using the moving particle semi-implicit (MPS) method
• A low-dimensional Galerkin method for the three-dimensional flow around a circular cylinder
• Learning representations by back-propagating errors
• General Introduction

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