The goal of Lu Group’s research is to model and simulate physical and biological systems at different scales by integrating modeling, simulation, and machine learning, and to provide strategies for system learning, prediction, optimization, and decision making in real time. Our current research interest lies in scientific machine learning, including theory, algorithms, and software, and its applications to engineering, physical, and biological problems. Our broad research interests focus on multiscale modeling and high performance computing for physical and biological systems.
PhD, Masters, & Undergraduate Students and Postdoc Opening: We are looking for PhD, Masters, and undergraduate students and Postdocs to work on scientific machine learning. Students in chemical engineering, mechanical engineering, applied mathematics, applied physics, or related majors with proficient coding skills are welcome to apply. Please feel free to contact me with CV (and/or transcripts, sample publications) attached if you are interested. For more information, please check the PhD programs in Chemical and Biomolecular Engineering and Applied Mathematics and Computational Science.
Recent News
- Congratulations to Mitchell Daneker and Min Zhu on passing the PhD qualifying exam. (May 18, 2022)
- New paper on arXiv: Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport. (Apr. 14, 2022)
- New paper on Computer Methods in Applied Mechanics and Engineering: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. (Mar. 18, 2022)
- New paper on Computer Methods in Applied Mechanics and Engineering: A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. (Mar. 11, 2022)
- New paper on arXiv: MIONet: Learning multiple-input operators via tensor product. (Feb. 14, 2022)
- New paper on arXiv: Systems biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. (Feb. 3, 2022)
- New paper on SIAM Journal on Scientific Computing: Physics-informed neural networks with hard constraints for inverse design. (Nov. 21, 2021)
- Congratulations to Jeremy Yu on winning the Bronze medal (Computer Science) of 2021 S.-T. Yau High School Science Award USA. (Nov. 16, 2021)
- New paper on arXiv: A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. (Nov. 11, 2021)
- Welcome to the first cohort of Lu Group: Mitchell Daneker, Min Zhu, Shuai Meng, Handi Zhang, Anran Jiao, Chenxi Wu! We had our first group meeting today! (Nov. 11, 2021)
- New paper on arXiv: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. (Nov. 4, 2021)
- Lu joins the Penn Institute for Computational Science (PICS). (Sept. 20, 2021)
- Penn Engineering Today: Assistant Professor Lu Lu Joins Department of Chemical and Biomolecular Engineering. (Aug. 30, 2021)