74 citations · 233 across the 18 of their papers we have counts for
4 papers · 1 filter
NeuralFVM: Neural-physics-based Finite Volume Method for Turbulent Flows Using the - Model
Tingkai Xue, Yu Jiao, Te Ba +9
In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard - model designed for…
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
Boyang Chen, Claire E. Heaney, Jefferson L. M. A. Gomes +2
This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. The idea comes from the observation that convolutional layers c…
Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics
Boyang Chen, Claire E. Heaney, Christopher C. Pain
Numerical discretisations of partial differential equations (PDEs) can be written as discrete convolutions, which, themselves, are a key tool in AI libraries and used in convolutio…
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
Claire E. Heaney, Zef Wolffs, Jón Atli Tómasson +7
The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over…