Publications

[1] Pathak, S., Li, M., & Zou, Q. (2026). Bridge retrofit prioritization for seismic risk mitigation of transportation networks using graph neural Bayesian optimization. Submitted.

[2] Adhikari, M., Letchford, C., & Li, M. (2026). Reliability assessment of a low-rise structure under wind loading using the NIST aerodynamic database. Submitted.

[3] Sapkota, M., Li, M., & Li, B. (2026). Variational LSTM with augmented inputs: Nonlinear response history metamodeling with aleatoric and epistemic uncertainty. arXiv preprint arXiv:2604.01587.

[4] Li, M., Arunachalam, S., & Spence, S. M. (2024). A multi-fidelity stochastic simulation scheme for estimation of small failure probabilities. Structural Safety, 106, 102397.

[5] Li, M., Jia, G., Mahmoud, H., Yu, Y. H., & Tom, N. (2023). Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array. Applied Mathematical Modelling, 119, 465-485.

[6] Cheng, Z., Li, M., Jia, G., & Shi, Z. (2022). Adaptive Gaussian process with PCA for prediction of complex dispersion relations for periodic structures. European Journal of Mechanics-A/Solids, 93, 104547.

[7] Li, M., Jia, G., Cheng, Z., & Shi, Z. (2021). Generative adversarial network guided topology optimization of periodic structures via Subset Simulation. Composite Structures, 260, 113254.

[8] Li, M., & Jia, G. (2020). Bayesian updating of bridge condition deterioration models using complete and incomplete inspection data. Journal of Bridge Engineering, 25(3), 04020007.

[9] Li, M., Wang, R-Q., & Jia, G. (2020). Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs. Reliability Engineering & System Safety, 195, 106725.

[10] Li, M., & Jia, G. (2020). Multi-fidelity Gaussian process model integrating low- and high-fidelity data considering censoring. Journal of Structural Engineering, 146(3), 04019215.

[11] Li, M., Cheng, Z., Jia, G., & Shi, Z. (2019). Dimension reduction and surrogate based topology optimization of periodic structures. Composite Structures, 229, 111385.

[1] Li, M., Arunachalam, S., & Spence, S. M. (2023). Multi-fidelity Information Fusion for Efficient Estimation of Small Failure Probabilities Considering Multiple Limit States. ICASP14, Dublin, Ireland.

[2] Li, M., Jia, G., Mahmoud, H., Yu, Y-H., & Tom, T. (2022). Convolution-fed Gaussian process with active learning for probabilistic power prediction of large-scale wave farm. ICOSSAR 2021-2022, Shanghai, China.

[3] Li, M., Wang, Z., & Jia, G. (2022). Multi-fidelity surrogate model for efficient tsunami evacuation risk assessment. ICOSSAR 2021-2022, Shanghai, China.

[4] Li, M., & Jia, G. (2021). Age, state, and environment dependent non-homogeneous stochastic model for improved bridge deterioration prediction. IABMAS 2021, Sapporo, Japan.

[5] Li, M., Jia, G., & Wang, R-Q. (2019). Surrogate modeling for sensitivity analysis of models with high-dimensional outputs. ICASP13, Seoul, South Korea.