Welcome to the Uncertainty Quantification & Scientific Machine Learning Group!
Our research focuses on developing computational methods of uncertainty quantification and machine learning for complex systems in science, engineering, and medicine. We seek to derive these methods under a rigorous framework of mathematics and statistics together with computational feasibility and scalability. Our projects entail answering questions such as:
- How much uncertainty accompanies the model prediction, and how can we reduce it from new data/evidence?
- What data should we acquire next, and how many?
- How can machine learning be used together with physical modeling?
- (2023/09) Congratulations to Yossi Cohen for being selected to the Rising Stars in Mechanical Engineering workshop at Berkeley!
- (2023/09) Chengyang Huang is giving a presentation “FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning — A Physics-Constrained Approach to Modeling Markov Decision Processes” at MMLDE-CSET.
- (2023/09) Our preprint “Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems” is available on arXiv.
- (2023/09) Congratulations to Aniket Jivani for winning the 2023 SHINE Outstanding Student Poster Contest!
- (2023/09) Yossi Cohen gave a presentation “To Trust or Not: Towards Efficient Uncertainty Quantification for Stochastic Shapley Explanations” at PHMAP. [paper]
- (2023/08) Our paper “A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks” is published in Structural and Multidisciplinary Optimization.
- (2023/08) Our MURI project has been funded by the ONR! (More: 1, 2)
- (2023/08) Congratulations to Chengyang Huang for receiving the Michigan Institute for Computational Discovery and Engineering (MICDE) Graduate Fellowship!
- (2023/08) Our paper “Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning” is published in Computer Methods in Applied Mechanics and Engineering.
- (2023/08) Our paper “Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation” is published in International Journal of Prognostics and Health Management.
- (2023/08) Congratulations to Zhiyi Chen for successfully defending his Ph.D. thesis!
- (2023/07) Celebrating Wanggang’s defense with great food at ShiangMi!
- (2023/07) Congratulations to Wanggang Shen for successfully defending his Ph.D. thesis!
We are grateful to all our current and past sponsors.