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, 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.
Ph.D., Master’s, & Undergraduate Students and Postdoc Opening: We are looking for Ph.D., Master’s, and undergraduate students, and Postdocs to work on scientific machine learning (SciML) and artificial intelligence for science (AI4Science). Students in chemical engineering, mechanical engineering, mathematics, physics, computer science, 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 Ph.D. programs in Chemical and Biomolecular Engineering and Applied Mathematics and Computational Science.
Recent News
- New paper on arXiv: Reliable extrapolation of deep neural operators informed by physics or sparse observations. (Dec. 13, 2022)
- New paper on arXiv: Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics. (Nov. 29, 2022)
- Congratulations to Benjamin Fan and Edward Qiao on winning the Honorable Mention (Economics and Financial Modeling) of 2022 S.-T. Yau High School Science Award USA. (Nov. 21, 2022)
- New paper on SIAM Journal on Scientific Computing: MIONet: Learning multiple-input operators via tensor product. (Nov. 12, 2022)
- New paper on Computer Methods in Applied Mechanics and Engineering: A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. (Oct. 22, 2022)
- Welcome to Langchen Liu! Langchen is a first-year Ph.D. student in Applied Mathematics and Computational Science. (Aug. 15, 2022)
- Lu gave a plenary talk on DeepONet at Mathematical and Scientific Machine Learning (MSML). (Aug. 15, 2022)
- New paper on Physical Review Research: Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport. (June 13, 2022)
- Congratulations to Lu Lu on winning the DOE Early Career Award. (June 7, 2022)
- Congratulations to Mitchell Daneker and Min Zhu on passing the Ph.D. qualifying exam. (May 18, 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: 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)
- 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)
- 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)