Probabilistic physics-guided machine learning for fatigue data analysis. - Jie Chen 082; Yongming Liu (Expert Syst. Appl., 2021)

Neural Optimization Machine: A Neural Network Approach for Optimization. - Jie Chen 082; Yongming Liu (CoRR, 2022)

Physics-guided mixture density networks for uncertainty quantification. - Jie Chen 082; Yang Yu 063; Yongming Liu (Reliab. Eng. Syst. Saf., 2022)

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling. - Hengrui Zhang; Jie Chen 082; James M. Rondinelli; Wei Chen 041 (CoRR, 2023)

Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization. - Vispi Karkaria; Anthony Goeckner; Rujing Zha; Jie Chen 082; Jianjing Zhang; Qi Zhu 002; Jian Cao; Robert X. Gao; Wei Chen 041 (CoRR, 2024)

Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning. - Jie Chen 082; Pengfei Ou; Yuxin Chang; Hengrui Zhang; Xiao-Yan Li; Edward H. Sargent; Wei Chen 041 (CoRR, 2024)

Do Graph Neural Networks Work for High Entropy Alloys? - Hengrui Zhang; Ruishu Huang; Jie Chen 082; James M. Rondinelli; Wei Chen 041 (CoRR, 2024)

Uncertainty quantification driven machine learning for improving model accuracy in imbalanced regression tasks. - Tuba Dolar; Jie Chen 082; Wei Chen 041 (Expert Syst. Appl., 2025)

Graph representation of local environments for learning high-entropy alloy properties. - Hengrui Zhang; Ruishu Huang; Jie Chen 082; James M. Rondinelli; Wei Chen 041 (Mach. Learn. Sci. Technol., 2025)