Hongyi Xu

Assistant Professor

University of Connecticut

United States

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About

Dr. Hongyi Xu is an Assistant Professor at the University of Connecticut, USA. His research encompasses the design and analysis of metamaterials, machine learning applications in manufacturing processes, and advanced modeling of electrochemical systems. Recent articles highlight his work in areas such as stochastic microstructures, phononic metamaterials, and digital twins in electrochemistry.

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2021

Collaborators (2)

Osama R. Bilal

UNITED STATES

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Anson W.K. Ma

University of Connecticut

UNITED STATES

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Hongyi Xu

Research Interests

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About

Articles (20)

Designing Periodic Metamaterials Inspired by Stochastic Microstructures via Texture-Synthesis-Driven Explainable AI

Band gap analysis and prediction for phononic metamaterials with different spiral shapes based on transfer learning

Tuning a metamaterial switch without external stimuli

Multi-position metamaterial switch enables adjustable bandgaps for guiding elastic waves

Large language models for extrapolative modeling of manufacturing processes

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Scalable control of extraneously induced defects in in-field additive manufacturing

An uncertainty-aware deep learning framework-based robust design optimization of metamaterial units

Pressure effects on lithium anode/nickel-manganese-cobalt oxide cathode pouch cells through fixture design

A Microstructural Electrochemo-mechanical Model of High-nickel Composite Electrodes Towards Digital Twins to Bridge the Particle and Electrode-level Characterizations

3D printing confectionaries with tunable mechanical properties

Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials

Quantification and propagation of Aleatoric uncertainties in topological structures

Unveiling the Mechanical and Electrochemical Evolution of Nanosilicon Composite Anodes in Sulfide‐Based All‐Solid‐State Batteries

Harnessing structural stochasticity in the computational discovery and design of microstructures

Data-Driven Multiscale Science for Tread Compounding

Design of Phononic Bandgap Metamaterials based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating

Programmability of ultrathin metasurfaces through curvature

Unlocking Failure Mechanisms and Improvement of Practical Li–S Pouch Cells through In Operando Pressure Study

Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems

Collaborators (2)

Osama R. Bilal

Anson W.K. Ma

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