| Fatigue modeling using neural networks: A comprehensive review J Chen, Y Liu Fatigue & Fracture of Engineering Materials & Structures 45 (4), 945-979, 2022 | 112 | 2022 |
| Probabilistic physics-guided machine learning for fatigue data analysis J Chen, Y Liu Expert Systems with Applications 168, 114316, 2021 | 86 | 2021 |
| Equivalent surface defect model for fatigue life prediction of steel reinforcing bars with pitting corrosion J Chen, B Diao, J He, S Pang, X Guan International Journal of Fatigue 110, 153-161, 2018 | 72 | 2018 |
| Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning J Chen, Y Liu Additive Manufacturing 39, 101876, 2021 | 52 | 2021 |
| Multiaxial high-cycle fatigue life prediction under random spectrum loadings H Wei, P Carrion, J Chen, A Imanian, N Shamsaei, N Iyyer, Y Liu International Journal of Fatigue 134, 105462, 2020 | 43 | 2020 |
| Uncertainty quantification of fatigue SN curves with sparse data using hierarchical Bayesian data augmentation J Chen, S Liu, W Zhang, Y Liu International Journal of Fatigue 134, 105511, 2020 | 39 | 2020 |
| Lifetime distribution selection for complete and censored multi-level testing data and its influence on probability of failure estimates J He, J Chen, X Guan Structural and Multidisciplinary Optimization 62, 1-17, 2020 | 25 | 2020 |
| Piecewise stochastic rainflow counting for probabilistic linear and nonlinear damage accumulation considering loading and material uncertainties J Chen, A Imanian, H Wei, N Iyyer, Y Liu International Journal of Fatigue 140, 105842, 2020 | 23 | 2020 |
| Multi-fidelity data aggregation using convolutional neural networks J Chen, Y Gao, Y Liu Computer methods in applied mechanics and engineering 391, 114490, 2022 | 18 | 2022 |
| Thermal conductivity of metal coated polymer foam: Integrated experimental and modeling study R Dai, G Chandrasekaran, J Chen, C Jackson, Y Liu, Q Nian, B Kwon International Journal of Thermal Sciences 169, 107045, 2021 | 14 | 2021 |
| Uncertainty quantification of fatigue properties with sparse data using hierarchical Bayesian model J Chen, Y Liu AIAA Scitech 2020 Forum, 0680, 2020 | 14 | 2020 |
| Probabilistic bulk property estimation using multimodality surface non-destructive measurements for vintage pipes J Chen, D Ersoy, Y Liu Structural Safety 87, 101995, 2020 | 9 | 2020 |
| Multimodality data fusion for probabilistic strength estimation of aging materials using Bayesian networks J Chen, Y Liu AIAA Scitech 2020 Forum, 1653, 2020 | 9 | 2020 |
| Physics-guided mixture density networks for uncertainty quantification J Chen, Y Yu, Y Liu Reliability Engineering & System Safety 228, 108823, 2022 | 8 | 2022 |
| Data-driven sensitivity analysis for static mechanical properties of additively manufactured Ti–6Al–4V A Sharma, J Chen, E Diewald, A Imanian, J Beuth, Y Liu ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part b …, 2022 | 8 | 2022 |
| Neural optimization machine: A neural network approach for optimization J Chen, Y Liu arXiv preprint arXiv:2208.03897, 2022 | 7 | 2022 |
| Multi-fidelity neural optimization machine for Digital Twins J Chen, C Meng, Y Gao, Y Liu Structural and Multidisciplinary Optimization 65 (12), 340, 2022 | 6 | 2022 |
| Subcycle fatigue crack growth and equivalent initial flaw size model for fatigue life assessment under arbitrary loadings for Al-7075 S Shivankar, J Chen, Y Liu International Journal of Fatigue 156, 106685, 2022 | 6 | 2022 |
| Physics-guided machine learning for multi-factor fatigue analysis and uncertainty quantification J Chen, Y Liu AIAA Scitech 2021 Forum, 1242, 2021 | 6 | 2021 |
| Energy‐based time derivative damage accumulation model under uniaxial and multiaxial random loadings SC Tien, H Wei, J Chen, Y Liu Fatigue & Fracture of Engineering Materials & Structures 45 (1), 159-173, 2022 | 5 | 2022 |
Yongming LiuArizona State UniversityVerified email at asu.edu
