1-9 of 9
Zihan Wang
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Journal Articles

Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

Zihan Wang, Austin Bray, Kiarash Naghavi Khanghah, Hongyi Xu
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. February 2025, 147(2): 021706.
Paper No: MD-24-1409
https://doi.org/10.1115/1.4066128
Published Online: September 26, 2024
View Article titled, Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks
Open the PDF for Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks in another window
Topics: Design, Metamaterials, Voxel, Thermal conductivity, Graph neural networks
Journal Articles

Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model

Accepted Manuscript
Kiarash Naghavi Khanghah, Zihan Wang, Hongyi Xu
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-24-1176
https://doi.org/10.1115/1.4066095
Published Online: July 31, 2024
View Article titled, Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model
Open the PDF for Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model in another window
Topics: Large language models, Metamaterials, Design, Artificial intelligence, Computer-aided design, Databases, Engineering systems and industry applications, Machine learning, Shape optimization, Topology
Proceedings Papers

Phononic Metamaterial Design via Transfer Learning-Based Topology Optimization Framework

Zihan Wang, Ran Zhuang, Weikang Xian, Jiawei Tian, Ying Li, Shikui Chen, Hongyi Xu
Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A048, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-89932
https://doi.org/10.1115/DETC2022-89932
View Paper titled, Phononic Metamaterial Design via Transfer Learning-Based Topology Optimization Framework
Open the PDF for Phononic Metamaterial Design via Transfer Learning-Based Topology Optimization Framework in another window
Topics: Design, Energy gap, Metamaterials, Optimization, Topology
Journal Articles

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

Zihan Wang, Weikang Xian, M. Ridha Baccouche, Horst Lanzerath, Ying Li, Hongyi Xu
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. April 2022, 144(4): 041705.
Paper No: MD-21-1373
https://doi.org/10.1115/1.4053814
Published Online: February 22, 2022
View Article titled, Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating
Open the PDF for Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating in another window
Topics: Design, Energy gap, Metamaterials, Optimization
Proceedings Papers

A Gaussian Mixture Variational Autoencoder-Based Approach for Designing Phononic Bandgap Metamaterials

Zihan Wang, Weikang Xian, M. Ridha Baccouche, Horst Lanzerath, Ying Li, Hongyi Xu
Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A002, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-67629
https://doi.org/10.1115/DETC2021-67629
View Paper titled, A Gaussian Mixture Variational Autoencoder-Based Approach for Designing Phononic Bandgap Metamaterials
Open the PDF for A Gaussian Mixture Variational Autoencoder-Based Approach for Designing Phononic Bandgap Metamaterials in another window
Topics: Design, Energy gap, Metamaterials, Optimization
Journal Articles

Quantitative Representation of Aleatoric Uncertainties in Network-Like Topological Structural Systems

Zihan Wang, Hongyi Xu
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031713.
Paper No: MD-20-1545
https://doi.org/10.1115/1.4049522
Published Online: January 29, 2021
View Article titled, Quantitative Representation of Aleatoric Uncertainties in Network-Like Topological Structural Systems
Open the PDF for Quantitative Representation of Aleatoric Uncertainties in Network-Like Topological Structural Systems in another window
Topics: Topology, Uncertainty, Stochastic processes, Simulation
Proceedings Papers

Quantification of Uncertainties Distributed in Network-Like Systems

Zihan Wang, Hongyi Xu
Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A048, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22082
https://doi.org/10.1115/DETC2020-22082
View Paper titled, Quantification of Uncertainties Distributed in Network-Like Systems
Open the PDF for Quantification of Uncertainties Distributed in Network-Like Systems in another window
Topics: Simulation, Uncertainty, Uncertainty quantification, Additive manufacturing, Engineering systems and industry applications, Gaussian distribution, Heat conduction, Metamaterials, Stochastic processes
Proceedings Papers

Material Model Calibration by Deep Learning for Additively Manufactured Alloys

Zihan Wang, Hongyi Xu, Yang Li
Proc. ASME. ISFA2020, 2020 International Symposium on Flexible Automation, V001T07A007, July 8–9, 2020
Publisher: American Society of Mechanical Engineers
Paper No: ISFA2020-9640
https://doi.org/10.1115/ISFA2020-9640
View Paper titled, Material Model Calibration by Deep Learning for Additively Manufactured Alloys
Open the PDF for Material Model Calibration by Deep Learning for Additively Manufactured Alloys in another window
Topics: Additive manufacturing, Alloys, Calibration, Machine learning
Proceedings Papers

Numerical Study of the Combustion Characteristics of a Diesel Micro Pilot Ignited DI Gasoline Engine With Turbocharging and Cooled EGR

Yuhua (York) Zhu, Nameer Salman, Kevin Freeman, Ronald Reese, II, Zihan Wang, Riccardo Scarcelli, Sibendu Som
Proc. ASME. ICEF2013, Volume 1: Large Bore Engines; Advanced Combustion; Emissions Control Systems; Instrumentation, Controls, and Hybrids, V001T03A022, October 13–16, 2013
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2013-19170
https://doi.org/10.1115/ICEF2013-19170
View Paper titled, Numerical Study of the Combustion Characteristics of a Diesel Micro Pilot Ignited DI Gasoline Engine With Turbocharging and Cooled EGR
Open the PDF for Numerical Study of the Combustion Characteristics of a Diesel Micro Pilot Ignited DI Gasoline Engine With Turbocharging and Cooled EGR in another window
Topics: Combustion, Diesel, Exhaust gas recirculation, Gasoline engines, Gasoline, Ignition, Engines, Computational fluid dynamics, Computer simulation, Corporate average fuel economy