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?
- (2021/02) Our paper “Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data” is published in the Computer Methods in Applied Mechanics and Engineering.
- (2021/01) Congratulations to Snehal Prabhudesai for successfully passing the Ph.D. Qualifying Exam!
- (2021/01) Aniket Jivani gave a presentation “Uncertainty Quantification for a Turbulent Round Jet Using Multifidelity Karhunen-Loeve Expansions” at the AIAA SciTech Forum [paper].
- (2021/01) Wanggang Shen gave a presetation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient” at the WCCM-ECCOMAS Congress.
- News Archive
We are grateful to all our current and past sponsors.