Publications

Journal papers

(27) Zhang, Yiming, Chen Jia, Xiaojian Liu, Jinghua Xu, Bingkun Guo, Yang Wang, and Shuyou Zhang. “Enhancing topology optimization with adaptive deep learning.” Computers & Structures 305 (2024): 107527.

(26) Qiu, Na, Zhuoqun Yu, Depei Wang, Mingwei Xiao, Yiming Zhang*, Nam H. Kim, and Jianguang Fang. “Bayesian optimization of origami multi-cell tubes for energy absorption considering mixed categorical-continuous variables.” Thin-Walled Structures 199 (2024): 111799.

(25) Xiang, Feifan, Yiming Zhang*, Shuyou Zhang, Zili Wang, Lemiao Qiu, and Joo-Ho Choi. “Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life.” Expert Systems with Applications 238 (2024): 121859.

(24) Yu, Zian, Yiming Zhang*, Shuyou Zhang, Guodong Yi, and Lemiao Qiu. “Semi-supervised auxiliary learning for surface defect detection and segmentation of injection-molded products from small image datasets.” The International Journal of Advanced Manufacturing Technology 131, no. 9 (2024): 5243-5264.

(23) Li, Ke, Shuyou Zhang, Yiming Zhang, Wenchen Yuan, and Genlin Mo. “A transformation method evaluate for near singular boundary integrals in the structural analysis of thin structure.” Engineering Analysis with Boundary Elements 160 (2024): 226-233.

(22) Yiming Zhang, Dingyang Zhang, Xiaoge Zhang, Lemiao Qiu, Felix TS Chan, Zili Wang, and Shuyou Zhang. “Guided probabilistic reinforcement learning for sampling-efficient maintenance scheduling of multi-component system.” Applied Mathematical Modelling 119 (2023): 677-697.

(21) Luo, Jiaqi, Zhen Fu, Yiming Zhang*, Wenhao Fu, and Jianjun Chen. “Aerodynamic optimization of a transonic fan rotor by blade sweeping using adaptive Gaussian process.” Aerospace Science and Technology 137 (2023): 108255.

(20) Yiming Zhang, Hongyi Zhang, Lemiao Qiu, Zili Wang, Shuyou Zhang, Na Qiu, and Jianguang Fang. “A stochastic framework for computationally efficient fail-safe topology optimization.” Engineering Structures 283 (2023): 115831.

(19) Yiming Zhang, Chen Jia, Hongyi Zhang, Naiyu Fang, Shuyou Zhang, Nam-Ho Kim. “ Improving Data-efficiency of Deep Generative Model for Fast Design Synthesis.” Journal of Mechanical Science and Technology (2024).

(18) Guo, Hongshuai, Jinghua Xu, Shuyou Zhang, Yiming Zhang, and Jianrong Tan. “Multi-orientation optimization of complex parts based on model segmentation in additive manufacturing.” Journal of Mechanical Science and Technology 37, no. 1 (2023): 317-331.

(17) Yiming Zhang, Zhiwei Pan, Shuyou Zhang, and Na Qiu. “Probabilistic invertible neural network for inverse design space exploration and reasoning.” Electronic Research Archive 31, no. 2 (2023): 860-881.

(16) Qiu, Lemiao, Huifang Zhou, Zili Wang, Yiming Zhang, Shuyou Zhang, and Longwu Pan. “Customized product design information feedback technology based on tentative design chain reconstruction.” Journal of Mechanical Science and Technology 36, no. 12 (2022): 6123-6133.

(15) Zhang, Huang, Shuyou Zhang, Lemiao Qiu, Yiming Zhang, Yang Wang, Zili Wang, and Gaopeng Yang. “A remaining useful life prediction method based on PSR-former.” Scientific Reports 12, no. 1 (2022): 17887.

(14) Zhang, Huang, Shuyou Zhang, Zili Wang, Lemiao Qiu, and Yiming Zhang. “Signals hierarchical feature enhancement method for CNN-based fault diagnosis.” Advances in Mechanical Engineering 14, no. 9 (2022): 16878132221125019.

(13) Qiu, Na, Jiazhong Zhang, Feiquan Yuan, Zhiyang Jin, Yiming Zhang, and Jianguang Fang. “Mechanical performance of triply periodic minimal surface structures with a novel hybrid gradient fabricated by selective laser melting.” Engineering Structures 263 (2022): 114377.

(12) Yiming Zhang, Sreekar Karnati, Soumya Nag, Neil Johnson, Genghis Khan, and Brandon Ribic. “Accelerating Additive Design With Probabilistic Machine Learning.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 8, no. 1 (2022): 011109.

(11) Nag, Soumya, Yiming Zhang, Sreekar Karnati, Lee Kerwin, Alex Kitt, Eric MacDonald, Dora Cheung, and Neil Johnson. “Probabilistic machine learning assisted feature-based qualification of DED Ti64.” JOM 73, no. 10 (2021): 3064-3081.

(10) Zhang, Y, Karnati, Sreekar, Nag, Soumya, Johnson, Neil, Brandon Ribic, “Accelerating Additive Design with Probabilistic Machine Learning”, ASCE-ASME Journal of Risk Uncertainty in Engineering Systems: Part B. Mechanical Engineering (2021)

(9) Subber, W., Ghosh, S., Pandita, P., Zhang, Y., & Wang, L. (2020). Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems. Vibration, 2021.

(8) Ghosh, S., Pandita, P., Atkinson, S., Subber, W., Zhang, Y., Kumar, N., Chakrabarti, S., and Wang, L. 2020, “Advances in Bayesian Probabilistic Modeling for Industrial Applications.” ASME. ASME J. Risk Uncertainty Part B

(7) Zhang, Y., Kim, N. H., Palliyaguru, U. R., Schutte, J. F., & Haftka, R. T. 2020. “Reduced allowable strength of composite laminate for unknown distribution due to limited tests”. Journal of Composite Materials

(6) Jesper Kristensen, Waad Subber, Zhang Y., Sayan Ghosh, Natarajan Chennimalai Kumar, Genghis Khan and Liping Wang, “Industrial Applications of Intelligent Adaptive Sampling Methods for Multi-Objective Optimization”, Design Engineering and Manufacturing, 2019

(5) Zhang, Y., Kim, N.-H.and Haftka, R. T., 2019, “General Surrogate Adaptive Sampling using Interquartile Range for Design Space Exploration”, Journal of Mechanical Design

(4) Zhang, Y., Kim, N.-H., Park, C., and Haftka, R. T., 2018, “A Strategy for Adaptive Sampling When Sampling Cost Is Variable In Design Space”, AIAA journal

(3) Zhang, Y., Kim, N.-H., Park, C., and Haftka, R. T., 2018, “Multi-Fidelity Surrogate Based on Single Linear Regression,” AIAA journal

(2) Zhang, Y., Schutte, J., Seneviratne, W., Kim, N., and Haftka, R. , 2017, “Sampling by Exploration and Replication for Estimating Experimental Strength of Composite Structures,” AIAA Journal.

(1) Zhang, Y., Park, C., Kim, N. H., and Haftka, R. T., 2017, “Function Prediction at One Inaccessible Point Using Converging Lines,” Journal of Mechanical Design, 139(5), p. 051402.

Conference papers

(24) Zhiwei Pan, Yiming Zhang*, Shuyou Zhang, Probabilistic Diagnosis of Aviation Engine with Invertible Neural Network, the 2023 International Conference on Mechanical Design (ICMD2023), Chengdu, China, Oct 20-22, 2023

(23) Feifan Xiang, Yiming Zhang*, Shuyou Zhang, The Dual-encoder Transformer for Prediction of Aero-engine Remaining Useful Life with Uncertainty Quantification, the 2023 International Conference on Mechanical Design (ICMD2023), Chengdu, China, Oct 20-22, 2023

(22) Dingyang Zhang, Yiming Zhang*, Shuyou Zhang, Multi-Agent Deep Q-Learning for Maintenance Scheduling of Engineering System with Large-scale State Space, the 2023 International Conference on Mechanical Design (ICMD2023), Chengdu, China, Oct 20-22, 2023

(21) Steven Atkinson, Yiming Zhang, Liping Wang, “Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models”, AAAI – MLPS, 2021

(20) Soumya Nag, Yiming Zhang, Sreekar Karnati, “Probabilistic Machine Learning Assisted Study of Directed Energy Deposited Alloys”, TMS 2021 Annual Meeting and Exhibition, March 15-18, 2021

(19) Zhang Y, Sayan Ghosh, Thomas Vandeputte, Liping Wang, “Bayesian Optimization for Multi-objective High-Dimensional Turbine Aero Design”， ASME Turbo Expo, June 7-11, 2021

(18) Sayan Ghosh, Andrey Meshkov, Vipul Gupta, Piyush Pandita, Zhang Y, Liping Wang, “Uncertainty Quantification of Mesoscale Melt-Pool Model for Powder Bed Fusion Additive Manufacturing of Metals”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, Nashville, TN, 2021

(17) Sayan Ghosh, Piyush Pandita, Steven Atkinson, Zhang Y, Liping Wang, “A Comparative Study of Intelligent and Adaptive Design Optimization using Probabilistic Deep Learning Models”. Proceedings of the ASME 2020 IDETC/CIE, Aug 16-19 2020, St. Louis, MO

(16) Zhang Y, Jesper Kristensen, Waad Subber, Sayan Ghosh, Genghis Khan, Liping Wang, “Remarks for Scaling Up a General Gaussian Process to Model Large Dataset with Sub-models”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, Orlando, US. 2020

(15) Sayan Ghosh, Piyush Pandita, Waad Subber, Zhang Y, Liping Wang, “Efficient Bayesian Inverse Method Using Robust Gaussian Process For Design Under Uncertainty”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, Orlando, US. 2020

(14) Natarajan Chennimalai Kumar, Zhang Y. Liping Wang, Genghis Khan, “Application of Intelligent Experimental Design for Additive Manufacturing”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, Orlando, US. 2020

(13) Sayan Ghosh, Jesper Kristensen, Zhang Y., Waad Subber, Liping Wang, “A strategy for adaptive sampling of multi-fidelity gaussian process to reduce predictive uncertainty”, Proceedings of the ASME IDETC/CIE 2019, August 18-21, 2019, Anaheim, USA

(12) Zhang Y., Jesper Kristensen, Sayan Ghosh, Thomas Vandeputte, James Tallman, Genghis Khan, Liping Wang,” Finding Maximum Expected Improvement for High-Dimensional Design Optimization”, In AIAA Aviation Forum, June 17-21, Dallas, TX, US. 2019

(11) Charles Jekel, Zhang Y., Bogdon Grechuk, Raphael Haftka,” Comparison of Chebyshev’s Inequality and Non-parametric B-Basis to Estimate Failure Strength of Composite Open Hole Tension Tests”, The World Congress of Structural and Multidisciplinary Optimization, May 20-24, 2019, Beijing, China

(10) Zhang, Y, Nam H. Kim, Raphael T. Haftka,”General Surrogate Adaptive Sampling using Interquartile Range for Design Space Exploration”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, San Diego, US. 2019

(9) Zhang, Y, Sayan Ghosh, Isaac Asher, You Ling, Liping Wang, “Learning Nonstationary Response using Clustering and Local Gaussian Process”, In AIAA Modeling and Simulation Technologies Conference, Jan 7-11, San Diego, US. 2019

(8) Zhang, Y, Nam H. Kim, Chanyoung Park, Raphael T. Haftka, “A Strategy for Adaptive Sampling When Sampling Cost Is Variable In Design Space,” Asian Congress of Structural and Multidisciplinary optimization, Dalian, China, 2018.

(7) Zhang, Y., Kim, N.H., Park, C. and Haftka, R.T., 2018. ”Effect of Varying Test Cost on Design of Experiments”. In AIAA Modeling and Simulation Technologies Conference

(6) Zhang, Y., Neelaantan, A., Kumar,N., Park, C., Haftka, R., Kim, N., Lam, H., 2018. ”Multi-fidelity Surrogates of Abstract Application and Architecture Models for Predicting Application Performance”, PMBS, International Workshop on. IEEE, 2017.

(5) Zhang, Y., Meeker, J., Schutte, J., Kim, N. and Haftka, R., 2017. “Predicting B-Basis Allowable at Untested Points from Experiments and Simulations of Plates with Holes”, 12th World Congress on Structural and Multidisciplinary Optimization, June, Germany

(4) Zhang, Y., Meeker, J., Schutte, J., Kim, N. and Haftka, R., 2016. On Approaches to Combine Experimental Strength and Simulation with Application to Open-Hole-Tension Configuration. In Proceedings of the American Society for Composites: Thirty-First Technical Conference.

(3) Zhang, Y., Kim, N.H., Park, C. and Haftka, R.T., 2016. Function Extrapolation of Noisy Data using Converging Lines. In AIAA Modeling and Simulation Technologies Conference (p. 2144).

(2) Zhang, Y., Haftka, R. T., Kim, N.-H., Schutte, J. F., and Seneviratne, W. P., 2015. “Allocation of Samples Between Exploration and Replication for Open-Hole-Tension Test,” Proc. American Society of Composites-30th Technical Conference.

(1) Zhang, Y., Kim, N., Park, C., and Haftka, R., 2015. “Function Extrapolation at One Inaccessible Point Using Converging Lines,” Proc. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, ASME