26 citations · 86 across the 9 of their papers we have counts for
10 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…
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…
A symbolic approach to discrete structural optimization using quantum annealing
Kevin Wils, Boyang Chen
With the advent of novel quantum computing technologies, and the knowledge that such technology might be used to fundamentally change computing applications, a prime opportunity ha…
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
Xuchen You, Shouvanik Chakrabarti, Boyang Chen +1
A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised le…
Solving the Discretised Boltzmann Transport Equations using Neural Networks: Applications in Neutron Transport
T. R. F. Phillips, C. E. Heaney, C. Boyang +2
In this paper we solve the Boltzmann transport equation using AI libraries. The reason why this is attractive is because it enables one to use the highly optimised software within…
Solving the Discretised Neutron Diffusion Equations using Neural Networks
T. R. F. Phillips, C. E. Heaney, C. Boyang +2
This paper presents a new approach which uses the tools within Artificial Intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs…
Intrinsic randomness under general quantum measurements
Hao Dai, Boyang Chen, Xingjian Zhang +1
Quantum measurements can produce randomness arising from the uncertainty principle. When measuring a state with von Neumann measurements, the intrinsic randomness can be quantified…