1 citations · 1 across the 4 of their papers we have counts for
9 papers
Parameter estimation for land-surface models using Neural Physics
Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk
We propose a novel inverse-modelling approach that estimates the parameters of a simple land-surface model (LSM) by assimilating data into a differentiable, physics-based forward m…
Inpainting U-Net for seamless pedestrian-level wind prediction across urban morphologies
Jingzi Huang, Claire E. Heaney, Tao Li +3
Pedestrian-level wind prediction is essential for urban design and wind-comfort assessment, but high-fidelity simulations such as LES remain computationally expensive for rapid eva…
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4
Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the i…
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…
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…
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…