Dr. Luning Sun is a Postdoctoral Researcher at the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory (LLNL), where he develops machine learning methodologies for complex scientific and engineering systems. His work bridges deep generative modeling, uncertainty quantification, and physics-informed learning to enable robust and interpretable surrogate models for multiscale physical dynamics. At LLNL, he has contributed to projects spanning plasma control, coarse-grained material modeling, and multi-fidelity soil science.

Dr. Sun received his Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame in 2023 under the supervision of Prof. Jian-Xun Wang. His doctoral research focused on integrating physical priors into data-driven models for flow field prediction, stochastic PDE modeling, and equation discovery. He also holds an M.S. in Civil Engineering from Notre Dame, where he worked on numerical modeling of wave dynamics, and a B.S. in Civil Engineering from Tianjin University.

His technical expertise spans:

• Scientific machine learning for PDEs and dynamical systems

• Generative modeling (e.g., diffusion models) for spatiotemporal data

• Physics-constrained deep learning

• Bayesian inference and sparse system identification

• Surrogate modeling and uncertainty quantification

Dr. Sun is an active reviewer for major ML and physics venues including NeurIPS, ICLR, ICML and AAAI , and has presented his work at NeurIPS, APS-DFD, SIAM UQ, and CSE conferences.

Ph.D., Aerospace and Mechanical Engineering, University of Notre Dame, South Bend, Indiana

M.S. Civil Engineering, University of Notre Dame, South Bend, Indiana

B.S. Civil Engineering, Tianjin University, Tianjin, China

Peer-Reviewed Journal Articles and Conference Papers

• Sun, L., Han, X., Gao, H., Wang, J.-X., & Liu, L.-P. (2023). Unifying Predictions of Deterministic and Stochastic Physics in Mesh-Reduced Space with Sequential Flow Generative Model. NeurIPS 2023, Spotlight Presentation.

• Gao, H., Han, X., Fan, X., Sun, L., Liu, L.-P., Duan, L., & Wang, J.-X. (2024). Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. Computer Methods in Applied Mechanics and Engineering, 427, 117023.

• Adeli, E., Sun, L., Wang, J.-X., & Taflanidis, A. (2023). An Advanced Spatio-Temporal Convolutional Recurrent Neural Network for Storm Surge Predictions. Neural Computing and Applications, 35, 10781–10797.

• Sun, L., Huang, D. Z., Hao, S., & Wang, J.-X. (2022). Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. NeurIPS 2022, Poster.

• Sun, L., Gao, H., Pan, S., & Wang, J.-X. (2020). Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data. Computer Methods in Applied Mechanics and Engineering, 361, 112732.

• Sun, L., & Wang, J.-X. (2020). Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data. Theoretical and Applied Mechanics Letters, 10(3), 161–169.

• Gao, H., Sun, L., & Wang, J.-X. (2021). Super-Resolution and Denoising of Fluid Flow Using Physics-Informed CNNs Without High-Resolution Labels. Physics of Fluids, 33(7), 073603.

• Zhang, Y., Jiang, W., Sun, L., Wang, J.-X., Zheng, X., & Xue, Q. (2022). A Deep Learning-Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation. Journal of Biomechanical Engineering, 144(9), 091001.

• Ma, R., Zhang, H., Xu, J., Sun, L., Hayashi, Y., Yoshida, R., Shiomi, J., Wang, J.-X., & Luo, T. Machine Learning-Assisted Exploration of Thermally Conductive Polymers. Materials Today Physics (in press).

Manuscripts Under Review

• Sun, L., Liu, X.-Y., Zhao, S., Grover, A., Wang, J.-X., & Thiagarajan, J. J. Multi-Fidelity Reinforcement Control for Complex Dynamical Systems. (Under review, 2025).

 Service and Outreach

• NeurIPS Scholar Award, NeurIPS 2023 and 2022 – Recognized for high-quality submissions and research impact

• Top Reviewer, NeurIPS 2024 – Acknowledged for excellence in peer review

• Student Travel Award, SIAM UQ22 – The SIAM Conference on Uncertainty Quantification

• Graduate School Professional Development Award, University of Notre Dame (2022)

• GSG Conference Presentation Grant, University of Notre Dame (2022)

• Best Poster & Oral Presentations – APS Division of Fluid Dynamics (multiple years)

Service and Outreach

• Session Chair, APS Division of Fluid Dynamics Annual Meeting (APS-DFD), 2024

• Reviewer, Signal, Image and Video Processing, 2025

• Reviewer, NeurIPS, ICLR, ICML, AAAI, AISTATS (2023–2024)

• Reviewer, Scientific Reports (2023)

• Reviewer, Journal of the Brazilian Society of Mechanical Sciences and Engineering (2023)

• Reviewer, Canadian Journal of Physics (2021)

• Volunteer, 75th Annual Meeting of the APS Division of Fluid Dynamics (2022)