73 citations · 189 across the 16 of their papers we have counts for
16 papers
Towards Universal Mesh Movement Networks
Mingrui Zhang, Chunyang Wang, Stephan Kramer +5
Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movemen…
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…
Complex Locomotion Skill Learning via Differentiable Physics
Yu Fang, Jiancheng Liu, Mingrui Zhang +6
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited…
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…
Aesthetic Photo Collage with Deep Reinforcement Learning
Mingrui Zhang, Mading Li, Li Chen +1
Photo collage aims to automatically arrange multiple photos on a given canvas with high aesthetic quality. Existing methods are based mainly on handcrafted feature optimization, wh…
Scalable Projection-Free Optimization
Mingrui Zhang
As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community. In this…
Unsupervised Learning of Particle Image Velocimetry
Mingrui Zhang, Matthew D. Piggott
Particle Image Velocimetry (PIV) is a classical flow estimation problem which is widely considered and utilised, especially as a diagnostic tool in experimental fluid dynamics and…
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…
One Sample Stochastic Frank-Wolfe
Mingrui Zhang, Zebang Shen, Aryan Mokhtari +2
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wi…
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
Yumeng Zhang, Gaoguo Jia, Li Chen +2
There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating…
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…
Projection-Free Bandit Convex Optimization
Lin Chen, Mingrui Zhang, Amin Karbasi
In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO). We show that our algorithm achieves a sublinear regret…