most citedRoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data

5 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

DreamTexture: Shape from Virtual Texture with Analysis by Augmentation

Ananta R. Bhattarai, Xingzhe He, Alla Sheffer +1

DreamFusion established a new paradigm for unsupervised 3D reconstruction from virtual views by combining advances in generative models and differentiable rendering. However, the u…

cs.RO2020

Soft Multicopter Control using Neural Dynamics Identification

Yitong Deng, Yaorui Zhang, Xingzhe He +5

Dynamic control of a soft-body robot to deliver complex behaviors with low-dimensional actuation inputs is challenging. In this paper, we present a computational approach to automa…

physics.comp-ph20205 cited

RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data

Shiying Xiong, Xingzhe He, Yunjin Tong +2

We introduce Roe Neural Networks (RoeNets) that can predict the discontinuity of the hyperbolic conservation laws (HCLs) based on short-term discontinuous and even continuous train…

cs.NE2020

Learning Physical Constraints with Neural Projections

Shuqi Yang, Xingzhe He, Bo Zhu

We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints. A neural projection operator lies at the heart o…

cs.CV2020

AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing

Xingzhe He, Helen Lu Cao, Bo Zhu

This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture…