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Shun-Peng Zhu

Showing results (1-10 of 13) with videos related to

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Materials (Basel, Switzerland)|August 5, 2017
A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine BladesShun-Peng Zhu, Peng Yue, Zheng-Yong Yu, et al.
Materials (Basel, Switzerland)|August 5, 2017
A New Energy-Critical Plane Damage Parameter for Multiaxial Fatigue Life Prediction of Turbine BladesZheng-Yong Yu, Shun-Peng Zhu, Qiang Liu, et al.
Materials (Basel, Switzerland)|August 10, 2017
Multiaxial Fatigue Damage Parameter and Life Prediction without Any Additional Material ConstantsZheng-Yong Yu, Shun-Peng Zhu, Qiang Liu, et al.
Polymers|March 27, 2020
Reliability Analysis of FRP-Confined Concrete at Ultimate using Conjugate Search Direction MethodBehrooz Keshtegar, Aliakbar Gholampour, Togay Ozbakkaloglu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|September 24, 2023
Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materialsLanyi Wang, Shun-Peng Zhu, Changqi Luo, et al.
Materials (Basel, Switzerland)|January 8, 2020
Advanced Simulation Tools Applied to Materials Development and Design PredictionsJosé Correia, Abílio De Jesus, Shun-Peng Zhu, et al.
Environmental Science and Pollution Research International|November 10, 2019
SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporationBehrooz Keshtegar, Salim Heddam, Abderrazek Sebbar, et al.
Thescientificworldjournal|February 28, 2014
A modified nonlinear damage accumulation model for fatigue life prediction considering load interaction effectsHuiying Gao, Hong-Zhong Huang, Shun-Peng Zhu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|September 24, 2023
Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'Shun-Peng Zhu, Abílio M P De Jesus, Filippo Berto, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|November 19, 2023
Preface to the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'Shun-Peng Zhu, Abílio M P De Jesus, Filippo Berto, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Materials (Basel, Switzerland)|August 5, 2017
A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine BladesShun-Peng Zhu, Peng Yue, Zheng-Yong Yu, et al.
Materials (Basel, Switzerland)|August 5, 2017
A New Energy-Critical Plane Damage Parameter for Multiaxial Fatigue Life Prediction of Turbine BladesZheng-Yong Yu, Shun-Peng Zhu, Qiang Liu, et al.
Materials (Basel, Switzerland)|August 10, 2017
Multiaxial Fatigue Damage Parameter and Life Prediction without Any Additional Material ConstantsZheng-Yong Yu, Shun-Peng Zhu, Qiang Liu, et al.
Polymers|March 27, 2020
Reliability Analysis of FRP-Confined Concrete at Ultimate using Conjugate Search Direction MethodBehrooz Keshtegar, Aliakbar Gholampour, Togay Ozbakkaloglu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|September 24, 2023
Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materialsLanyi Wang, Shun-Peng Zhu, Changqi Luo, et al.
Materials (Basel, Switzerland)|January 8, 2020
Advanced Simulation Tools Applied to Materials Development and Design PredictionsJosé Correia, Abílio De Jesus, Shun-Peng Zhu, et al.
Environmental Science and Pollution Research International|November 10, 2019
SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporationBehrooz Keshtegar, Salim Heddam, Abderrazek Sebbar, et al.
Thescientificworldjournal|February 28, 2014
A modified nonlinear damage accumulation model for fatigue life prediction considering load interaction effectsHuiying Gao, Hong-Zhong Huang, Shun-Peng Zhu, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|September 24, 2023
Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'Shun-Peng Zhu, Abílio M P De Jesus, Filippo Berto, et al.
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|November 19, 2023
Preface to the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'Shun-Peng Zhu, Abílio M P De Jesus, Filippo Berto, et al.
Pageof 2