6 citations · 16 across the 6 of their papers we have counts for
8 papers
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
Jiaxin Zhang, Sirui Bi, Victor Fung
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a…
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizi…
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intrac…
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…
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach
Xianglin Liu, Jiaxin Zhang, Junqi Yin +3
High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is i…
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Local-gradient-based optimization approaches lack nonlocal exploration ability required for escaping from local minima in non-convex landscapes. A directional Gaussian smoothing (D…