25 citations · 52 across the 10 of their papers we have counts for
10 papers
On the Quantification of Image Reconstruction Uncertainty without Training Data
Sirui Bi, Victor Fung, Jiaxin Zhang
Computational imaging plays a pivotal role in determining hidden information from sparse measurements. A robust inverse solver is crucial to fully characterize the uncertainty indu…
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…
Atomic structure generation from reconstructing structural fingerprints
Victor Fung, Shuyi Jia, Jiaxin Zhang +3
Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help…
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…
Robust data-driven approach for predicting the configurational energy of high entropy alloys
Jiaxin Zhang, Xianglin Liu, Sirui Bi +3
High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially charact…
Chemical complexity in high entropy alloys: A pair-interaction perspective
Xianglin Liu, Jiaxin Zhang, Sirui Bi +3
The recently proposed pair-interaction model is applied to study a series of refractory high entropy alloys. The results demonstrate the simplicity, robustness, and high accuracy o…