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Grants & Projects
- 2 Active
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Collaborative Research: CAIG: Inferring Hidden Urban Flood Controls Using Physics-Informed Multimodal Learning
Pei, T. (PI) & Cascante Bonilla, P. (CoPI)
10/1/26 → 09/30/29
Project: Research
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CAIG: Unifying Landslide Domain Knowledge and AI to Understand Landslide Causality
Pei, T. (PI)
10/1/25 → 09/30/27
Project: Research
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A multi-objective physics-informed machine learning framework for landslide susceptibility mapping
Cui, H., Pei, T., Devineni, N., Tian, Y., Shen, C. & Ji, J., 2026, In: Georisk. 20, 2, p. 531-555 25 p.Research output: Contribution to journal › Article › peer-review
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Comparison of different machine learning models coupling with logistic regression for landslide susceptibility mapping
Ji, J., Deng, J., Cui, H., Tong, B., Tang, X. & Pei, T., Jun 2026, In: Gondwana Research. 154, p. 155-172 18 p.Research output: Contribution to journal › Article › peer-review
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Differentiable machine learning in geotechnical engineering: A case study of bearing capacity prediction of shallow foundation on cohesionless soils
Xiong, J. & Pei, T., 2026, Geo-Congress 2026: Soil Properties, Modeling, and Computational Geomechanics - Selected papers from Geo-Congress 2026. Montgomery, J. & Cox, B. R. (eds.). American Society of Civil Engineers (ASCE), p. 103-112 10 p. (Geo-Congress 2026: Soil Properties, Modeling, and Computational Geomechanics - Selected papers from Geo-Congress 2026).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Integrating explainable ensemble machine learning with hydrodynamic modeling to assess tropical cyclone-induced flood susceptibility
Liu, Q., Gao, L., Du, H., Pei, T., Kuok, S. C. & Chen, S., Sep 2026, In: Geoscience Frontiers. 17, 5, 102355.Research output: Contribution to journal › Article › peer-review
Open Access -
基于概率物理信息的机器学习区域滑坡易发性评估
Translated title of the contribution :A physics-informed machine learning framework for landslide susceptibility assessment based on probabilistic physically-based model Research output: Contribution to journal › Article › peer-review
Open Access