activity
20182021
most citedMeshingNet: A New Mesh Generation Method based on Deep Learning

9 citations · 10 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CE2021

Robust design optimisation of continuous flow polymerase chain reaction thermal flow systems

Yongxing Wang, Hazim A. Hamad, Jochen Voss +1

This paper presents an efficient methodology for the robust optimisation of Continuous Flow Polymerase Chain Reaction (CFPCR) devices. It enables the effects of uncertainties in de…

math.NA20209 cited

MeshingNet: A New Mesh Generation Method based on Deep Learning

Zheyan Zhang, Yongxing Wang, Peter K. Jimack +1

We introduce a novel approach to automatic unstructured mesh generation using machine learning to predict an optimal finite element mesh for a previously unseen problem. The framew…

cs.CE2020

An energy stable one-field monolithic arbitrary Lagrangian-Eulerian formulation for fluid-structure interaction

Yongxing Wang, Peter K. Jimack, Mark A. Walkley +1

In this article we present a one-field monolithic finite element method in the Arbitrary Lagrangian-Eulerian (ALE) formulation for Fluid-Structure Interaction (FSI) problems. The m…

eess.SY2019

Optimal charging guidance strategies for electric vehicles by considering dynamic charging requests in a time-varying road network

Yongxing Wang, Jun Bi

Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving…

cs.CE20181 cited

An Energy Stable One-Field Fictitious Domain Method for Fluid-Structure Interactions

Yongxing Wang, Peter K. Jimack, Mark A. Walkley

In this article, the energy stability of a one-field fictitious domain method is proved and validated by numerical tests in two and three dimensions. The distinguishing feature of…