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 (SciML) and artificial intelligence for science (AI4Science), 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.
Opening: We are looking for Ph.D., Master’s, and undergraduate students, Postdocs, and Research Assistants to work on scientific machine learning (SciML) and artificial intelligence for science (AI4Science). Students in statistics, mathematics, physics, computer science, engineering, 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 Statistics and Data Science and Applied Mathematics, and postdoctoral positions in Data Science (Job posting), Applied Mathematics, and Neuroscience.
Recent News
- π° New paper on arXiv: ADEPT: A noninvasive method for determining elastic properties of valve tissue. (Oct. 1, 2024)
- π° New paper on arXiv: Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration. (Sept. 25, 2024)
- π° New paper on arXiv: Quantum DeepONet: Neural operators accelerated by quantum computing. (Sept. 24, 2024)
- πΈ We are awarded a new $4 Million grant from DOE ASCR as the Lead (funding rate 2%) to develop physics-informed and energy-aware federated learning of neural multi-operator learners as scientific foundation models, in collaboration with Leandros Tassiulas (Yale), Zecheng Zhang (Florida State), Yuanyuan Shi (UCSD), Youzuo Lin (UNC-Chapel Hill), Xiaoming Sun (LANL), James Benedict (LANL), David Alumbaugh (LBNL), and Evan Um (LBNL). (Sept. 6, 2024)
- πΈ We received a generous gift from FM to develop scientific machine learning models for fire. (Sept. 6, 2024)
- π° New paper on Computational Economics: Deep learning for solving and estimating dynamic macro-finance models. (Aug. 9, 2024)
- π° New paper on Small Methods: Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks. (Aug. 2, 2024)
- π° New paper on Reliability Engineering & System Safety: Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration. (July 28, 2024)
- π Congratulations to our Masterβs students Handi and Ziyi, who are heading to Penn and Northwestern for Ph.D. study, respectively! (July 18, 2024)
- π° New paper on arXiv: Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators. (July 9, 2024)
- π Congratulations to our undergraduate student Andy on his graduation and his new position at Citadel Securities! (July 7, 2024)
- π Congratulations to our high school graduates Eric and Jacob, who are heading to MIT and Brown for undergraduate study, respectively! (July 2, 2024)
- πΈ We are awarded a new grant from NSF DMS/NIGMS as the Lead to understand genomic organization in the nucleus via biophysical modeling, super-resolution imaging, and data-efficient operator learning, in collaboration with Vivek Shenoy (Penn). (June 21, 2024)
- π Our paper on tracking hemodynamics via PINNs has been selected as the Cover Article of Nexus. (June 19, 2024)
- π Our paper on training PINNs has been selected for an oral presentation (Top 1.5%) at ICML 2024. (June 12, 2024)
- π° New paper on Computer Methods in Applied Mechanics and Engineering: Speeding up and reducing memory usage for scientific machine learning via mixed precision. (June 7, 2024)
- π° New paper on Nexus: Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta. (May 23, 2024)
- πΈ We are awarded a new grant from DOE to enhance silicon solar cells using data-informed modeling, in collaboration with Sumit Agarwal (Mines), Talid Sinno (Penn), and David Young (NREL). (May 1, 2024)
- π DeepXDE is the Most Read article in SIAM Review. (Feb. 23, 2024)
- π° New paper on arXiv: Speeding up and reducing memory usage for scientific machine learning via mixed precision. (Jan. 31, 2024)
- β¨ Lu Lu gave a talk on “Accurate, efficient, and reliable learning of deep neural operators” at Stanford University, Department of Mechanical Engineering, Mechanics and Computation Seminar. (Dec. 7, 2023)
- β¨ NVIDIA Deep Learning Institute has launched the Science and Engineering Teaching Kit developed by George Karniadakis, Raj Shukla, and Lu Lu in collaboration with NVIDIA. This course is available for free download for universities from the NVIDIA websites (here and here). For more information, please check out the news. (Nov. 13, 2023)
- π Highlight on AAS Nova: DeepONet offers an easier, faster way to model planet-forming disks. See the paper in Astrophysical Journal Letters. (Oct. 9, 2023)
- π Congratulations to Anran Jiao on winning the Master’s Outstanding Academic Award of Penn Engineering. (May 11, 2023)
- π 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)
- β¨ Lu Lu gave a plenary talk on “Learning operators using deep neural networks for multiphysic, multiscale, and multifidelity problems” at Mathematical and Scientific Machine Learning (MSML). Watch the talk on YouTube. (Aug. 15, 2022)
- π πΈ Congratulations to Lu Lu on winning the DOE Early Career Award. (June 7, 2022)
- π Congratulations to Jeremy Yu on winning the Bronze Medal (Computer Science) of 2021 S.-T. Yau High School Science Award USA. (Nov. 16, 2021)