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?
- (2021/03) Congratulations to Aniket Jivani for successfully passing the Ph.D. Qualifying Exam!
- (2021/03) Congratulations to Codie Kawaguchi for winning the NSF Graduate Research Fellowships Program (GRFP) award!
- (2021/03) SIAM Conference on Computaitonal Science and Engineering
- Aniket Jivani gave a presentation “Multifidelity Karhunen-Loeve Expansions for Uncertainty Propagation of Random Field Quantities“
- Maria Veiga gave a presentation “Including Physical Knowledge into Data Driven Models: Applications in Computational Astrophysics“
- Saibal De presented a poster “Tensor-Train Decomposition for Data Compression and Data-Driven Reduced Order Modeling“
- Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”
- Xun Huan gave a presentation “Optimal Experimental Design for Variational System Identification of Material Physics Phenomena”
- Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments using Reinforcement Learning with Policy Gradient Methods“
- Xun Huan co-organized a minisymposium “Model-Based Optimal Experimental Design” (part 1, 2)
- (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.
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We are grateful to all our current and past sponsors.