11 citations · 39 across the 10 of their papers we have counts for
16 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…
Inverse design of two-dimensional materials with invertible neural networks
Victor Fung, Jiaxin Zhang, Guoxiang Hu +2
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sam…
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…
Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics
Jiaxin Zhang, Congjie Wei, Chenglin Wu
For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanica…