7 citations · 18 across the 6 of their papers we have counts for
7 papers
Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data
Zhongshun Shi, Hang Ma, Hoang Tran +1
The identification of governing equations for dynamical systems is everlasting challenges for the fundamental research in science and engineering. Machine learning has exhibited gr…
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
Yu Xiang, Yu Huang, Haodong Xu +2
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model…
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Sirui Bi, Jiaxin Zhang, Guannan Zhang
Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the ap…
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
Hoang Tran, Guannan Zhang
The local gradient points to the direction of the steepest slope in an infinitesimal neighborhood. An optimizer guided by the local gradient is often trapped in local optima when t…
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Jiaxing Zhang, Hoang Tran, Guannan Zhang
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limi…
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Jiaxin Zhang, Hoang Tran, Dan Lu +1
We propose an improved evolution strategy (ES) using a novel nonlocal gradient operator for high-dimensional black-box optimization. Standard ES methods with -dimensional Gaussi…