73 citations · 189 across the 16 of their papers we have counts for
8 papers · 1 filter
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
Siyi Li, Mingrui Zhang, Matthew D. Piggott
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a sur…
E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation
Joseph G. Wallwork, Jingyi Lu, Mingrui Zhang +1
Given a partial differential equation (PDE), goal-oriented error estimation allows us to understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and accumu…
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Mingrui Zhang, Jianhong Wang, James Tlhomole +1
Extracting information on fluid motion directly from images is challenging. Fluid flow represents a complex dynamic system governed by the Navier-Stokes equations. General optical…
M2N: Mesh Movement Networks for PDE Solvers
Wenbin Song, Mingrui Zhang, Joseph G. Wallwork +8
Mainstream numerical Partial Differential Equation (PDE) solvers require discretizing the physical domain using a mesh. Mesh movement methods aim to improve the accuracy of the num…
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen, Yifei Min, Mingrui Zhang +1
Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the d…
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Mingrui Zhang, Lin Chen, Hamed Hassani +1
In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2…
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Mingrui Zhang, Lin Chen, Aryan Mokhtari +2
How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and h…
Black Box Submodular Maximization: Discrete and Continuous Settings
Lin Chen, Mingrui Zhang, Hamed Hassani +1
In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is…