22 citations · 34 across the 7 of their papers we have counts for
8 papers
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…
Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
Jucai Zhai, Muhammad Abdullah, Boyang Chen +6
Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-preserving alternative to data-driven su…
An AI-driven framework for the prediction of personalised health response to air pollution
Nazanin Zounemat-Kermani, Sadjad Naderi, Claire H. Dilliway +10
Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-tim…
Coarsening and parallelism with reduction multigrids for hyperbolic Boltzmann transport
S. Dargaville, R. P. Smedley-Stevenson, P. N. Smith +1
Reduction multigrids have recently shown good performance in hyperbolic problems without the need for Gauss-Seidel smoothers. When applied to the hyperbolic limit of the Boltzmann…
Rapid modelling of reactive transport in porous media using machine learning: limitations and solutions
Vinicius L S Silva, Geraldine Regnier, Pablo Salinas +3
Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flo…
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…
AIR multigrid with GMRES polynomials (AIRG) and additive preconditioners for Boltzmann transport
S. Dargaville, R. P. Smedley-Stevenson, P. N. Smith +1
We develop a reduction multigrid based on approximate ideal restriction (AIR) for use with asymmetric linear systems. We use fixed-order GMRES polynomials to approximate $A_\textrm…