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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, Chemical & Environmental Engineering, and Applied & Computational Mathematics, and postdoctoral positions in Data Science (Job posting), Applied Mathematics, Computer Science, Data Science and Economics, Neuroscience, and Astronomy.

Recent News

  • 📰 New paper on Engineering Applications of Computational Fluid Mechanics: Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration. (Dec. 4, 2024)
  • 💸 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)
  • 💸 We received a generous gift from FM to develop scientific machine learning models for fire. (Sept. 6, 2024)
  • 💸 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)
  • 🎉 Our paper on tracking hemodynamics via PINNs has been selected as the Cover Article of Nexus. (June 19, 2024)
  • 💸 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)
  • 🎉 DeepXDE is the Most Read and Most Cited article in SIAM Review. (Feb. 23, 2024)