DDPS | Bridging Models and Data: Assimilation, Model Hierarchies, Causal Inference & Digital Twins скачать в хорошем качестве

DDPS | Bridging Models and Data: Assimilation, Model Hierarchies, Causal Inference & Digital Twins

DDPS Talk Date: October 30, 2025 Speaker: Nan Chen (University of Wisconsin-Madison) Title: Bridging Models and Data: From Traditional Assimilation to Bridging Model Hierarchies, Causal Inference, and Digital Twins Abstract: In this talk, I will present data assimilation as a crucial bridge between models and data across diverse scientific fields. I will begin with a brief review of traditional data assimilation before demonstrating its broad utility for facilitating and interacting with other areas of study and innovation. First, I will demonstrate how models of varying complexity from different communities can be integrated through a reconfigured latent data assimilation approach. In particular, I will illustrate how to leverage the strengths of idealized models and complex operational models to create a more accurate and cohesive system, with an application to the equatorial Pacific Ocean. Second, I will introduce assimilative causal inference (ACI), a new framework that uses Bayesian data assimilation to trace causes backward from observed effects, which provides a unique way to study predictability and attribution with applications in climate tipping points, model reduction, and extreme events. ACI uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and scales efficiently to high dimensions. It provides online tracking of causal roles, which may reverse intermittently, and establishes a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. Finally, I will present a nonlinear neural differential equation modeling framework that exploits generalized Koopman theory to discover a latent representation of state variables. This allows for closed-form solutions to nonlinear data assimilation and advances computationally efficient digital twins. Bio: Dr. Nan Chen is currently an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison. He is also affiliated with the Institute for Foundations of Data Science. Dr. Chen received his PhD from the Courant Institute of Mathematical Sciences, New York University. His PhD was in Applied Mathematics and Atmosphere and Ocean Science. He spent two more years as a Postdoctoral Research Associate at Courant before he moved to the University of Wisconsin-Madison. Dr. Chen’s research spans broadly across general applied mathematics, atmospheric and ocean science, materials science, and data science. He received the Kurt O. Friedrichs prize, the Silver Medal of Doctor Thesis for New World Mathematics Awards, and the ONR Young Investigator Award. His work has been featured in SIAM News and numerous media outlets. He is the secretary of SIAM MPE and one of the founders and the chair of the AGU session “Applied Math Perspectives on Modeling, Analyzing, and Predicting Complex Geophysical Systems”. 📚 DDPS seminar is organized by libROM team ( www.librom.net ). 🗓️ DDPS seminar schedule: https://www.librom.net/ddps.html 💻 LLNL News: https://www.llnl.gov/news 📲 Instagram: https://www.instagram.com/livermore_l... 🤳 Facebook:   / livermore.lab   🐤 Twitter: https://x.com/livermore_lab/ About LLNL: Lawrence Livermore National Laboratory has a mission of strengthening the United States’ security through development and application of world-class science and technology to: 1) enhance the nation’s defense, 2) reduce the global threat from terrorism and weapons of mass destruction, and 3) respond with vision, quality, integrity and technical excellence to scientific issues of national importance. Learn more about LLNL: https://www.llnl.gov/. IM release number: LLNL-VIDEO-2015726