96 citations · 502 across the 28 of their papers we have counts for
31 papers
Generative Evolutionary Design of Voxel-Based Soft Robots with Provable Optimality
Junru Song, Huan Xiao, Yang Yang +8
Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spaces and expensive evaluations…
RoboLDA: A Probabilistic Generative Model for Uncovering Embodied Hierarchical Structures in Voxel-based Soft Robots
Junru Song, Yang Yang, Jingdan Shi +6
Recent advances in robotics highlight hierarchical configurations of robot morphology, where multiple levels of functional substructures synergize to facilitate intelligent behavio…
Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning
Junru Song, Yang Yang, Yaqing Xu +5
Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evolution. Prior work has establi…
CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning
Junru Song, Wenhao Zhang, Yang Yang +7
Target-oriented vision-and-language navigation (VLN) on aerial platforms is attracting growing attention for missions such as disaster rescue, infrastructure inspection, and securi…
MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction
Siyu Ye, Shihang Li, Zhiqiang Gong +4
Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models exhibit promise for single-…
Learning to Reconstruct Temperature Field from Sparse Observations with Implicit Physics Priors
Shihang Li, Zhiqiang Gong, Weien Zhou +2
Accurate reconstruction of temperature field of heat-source systems (TFR-HSS) is crucial for thermal monitoring and reliability assessment in engineering applications such as elect…
Eva-VLA: Evaluating Vision-Language-Action Models' Robustness Under Real-World Physical Variations
Hanqing Liu, Shouwei Ruan, Jiahuan Long +6
Vision-Language-Action (VLA) models have emerged as promising solutions for robotic manipulation, yet their robustness to real-world physical variations remains critically underexp…
PapMOT: Exploring Adversarial Patch Attack against Multiple Object Tracking
Jiahuan Long, Tingsong Jiang, Wen Yao +5
Tracking multiple objects in a continuous video stream is crucial for many computer vision tasks. It involves detecting and associating objects with their respective identities acr…
HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling
YiFan Hao, Yang Yang, Junru Song +4
In the field of robotic control, designing individual controllers for each robot leads to high computational costs. Universal control policies, applicable across diverse robot morp…
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications
Donghua Wang, Wen Yao, Tingsong Jiang +3
Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's prod…
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Perception of the full state is an essential technology to support the monitoring, analysis, and design of physical systems, one of whose challenges is to recover global field from…
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Yunyang Zhang, Zhiqiang Gong, Weien Zhou +3
Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, larg…
Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks
Wei Peng, Wen Yao, Weien Zhou +2
Physics-informed neural networks (PINNs) have been proposed to solve two main classes of problems: data-driven solutions and data-driven discovery of partial differential equations…
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
Zhiqiang Gong, Ping Zhong, Jiahao Qi +1
Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic…
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
Xu Liu, Wen Yao, Wei Peng +1
Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial diffe…
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks
Wei Peng, Weien Zhou, Xiaoya Zhang +2
Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its fl…
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
Jialiang Sun, Xiaohu Zheng, Wen Yao +3
In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system…
Hybrid Finite Difference with the Physics-informed Neural Network for solving PDE in complex geometries
Zixue Xiang, Wei Peng, Weien Zhou +1
The physics-informed neural network (PINN) is effective in solving the partial differential equation (PDE) by capturing the physics constraints as a part of the training loss funct…
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
Xu Liu, Wei Peng, Zhiqiang Gong +2
Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have b…
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Donghua Wang, Tingsong Jiang, Jialiang Sun +5
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an in…
A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
Xiaoqian Chen, Zhiqiang Gong, Xiaoyu Zhao +2
Temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors occurred in thermal management plays an important role in real time health detecti…
A novel meta-learning initialization method for physics-informed neural networks
Xu Liu, Xiaoya Zhang, Wei Peng +2
Physics-informed neural networks (PINNs) have been widely used to solve various scientific computing problems. However, large training costs limit PINNs for some real-time applicat…
IDRLnet: A Physics-Informed Neural Network Library
Wei Peng, Jun Zhang, Weien Zhou +3
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This pa…
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
Zhiqiang Gong, Weien Zhou, Jun Zhang +2
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components…