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Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications
August 21–22, 2025 (Thursday–Friday), Discovery Building (330 N. Orchard Street, Madison)
This event is a collaboration with the Data Science Center at Brigham Young University (BYU), a partner of UW IFDS. We sincerely appreciate the generous support of NSF and the Institute for Foundation of Data Science at the University of Wisconsin-Madison.
This workshop brings together researchers and practitioners to explore the broad landscape of data assimilation, emphasizing both theoretical foundations and practical applications. On the theoretical front, the workshop will delve into topics such as nudging data assimilation and its connections to partial differential equations (PDEs), control theory, and error analysis. For practical methods, we will highlight a range of Bayesian data assimilation techniques, including the ensemble Kalman filter and the particle filter, which represent discrete-in-time approaches. Continuous-intime frameworks, such as nudging methods, conditional Gaussian nonlinear data assimilation, and the ensemble Kalman-Bucy filter, will also be discussed, with real-world applications in climate science, atmospheric and ocean modeling, and engineering systems. A key focus of the workshop is to strengthen interdisciplinary connections between data assimilation and tools such as machine learning, stochastic models, parameter estimation, optimal control, and model identification. By fostering discussions among different communities, the event aims to bridge gaps between theory and practice, encourage collaboration, and inspire new research directions. Additionally, the workshop will provide an excellent opportunity for young researchers to gain exposure to various methods, equipping them with tools to address challenges in complex dynamical systems.
The workshop will feature posters and lightning-talk sessions to provide students and junior researchers an opportunity to showcase their work. There will also be a limited amount of financial support for students and junior researchers. For those who want to attend this workshop and give a poster (together with a lightning) presentation, please fill out this form.
If you have any questions, please feel free to contact Dr. Nan Chen (chennan at math.wisc.edu).
Confirmed speakers:
Erik Bollt (Clarkson Univeristy)
Inverse Problems for processing satellite images – remote sensing of ocean. Smart filters for anisotropic noise characteristic of satellite imagry and specialized for data driven learning of ocean flows.
Elizabeth Carlson (Caltech)
Understanding Large-Time Behavior of Fluid Systems Using Data Assimilation & Optimization
Steven J. Fletcher (Colorado State University)
Non-Gaussian-based variational, Kalman Filters, and Ensemble-based Data Assimilation methods
Pedram Hassanzadeh (University of Chicago)
Learning extreme, rare dynamics from data in high-dimensional multi-scale systems such as weather
Chris Jones (University of North Carolina, Chapel Hill)
Adapting the data assimilation scheme to the computational model
Adam Larios (University of Nebraska–Lincoln)
Data Underground: Assimilation methods for water flow in the soil
Evelyn Lunasin (United States Naval Academy)
Data-Driven Model Identification Using Time Delayed Nonlinear Maps for Systems with Multiple Attractors
Vincent R. Martinez (The City University of New York Hunter College)
Some principles for state and parameter reconstruction in a class of nonlinear dissipative systems
Romit Maulik (Penn State University)
Divide and conquer: Learning chaotic dynamical systems with multistep penalty optimization
Jonathan Poterjoy (University of Maryland, College Park)
Practical approaches to assimilating observations that exhibit state-dependent, non-Gaussian errors in Earth-System models.
Daniel Sanz-Alonso (University of Chicago)
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
Peter Jan Van Leeuwen (Colorado State University)
Nonlinear data assimilation, physical nudging, and machine learning
Jinlong Wu (University of Wisconsin Madison)
Conditional Gaussian Koopman Network for Modeling Complex Systems and Data Assimilation
Confirmed participants:
| August 21 (Thursday) | |
| 8:50am-9:00am | Opening remarks |
| 9:00am-9:30am | Peter Jan Van Leeuwen |
| 9:35am-10:05am | Steven J. Fletcher |
| 10:10am-10:40am | Chris Jones |
| 10:45am-noon | Lightning talks |
| noon-1:30pm | Lunch break |
| 1:30pm-2:00pm | Pedram Hassanzadeh |
| 2:05pm-2:35pm | Jinlong Wu |
| 2:40pm-3:10pm | Romit Maulik |
| 3:15pm-3:45pm | Jonathan Poterjoy |
| 3:45pm-4:15pm | Discussions |
| August 22 (Friday) | |
| 9:00am-9:30am | Vincent R. Martinez |
| 9:35am-10:05am | Elizabeth Carlson |
| 10:10am-10:40am | Adam Larios |
| 10:45am-11:30am | Lightning talks |
| 11:30am-2:00pm | Lunch break & Poster session |
| 2:00pm-2:30pm | Erik Bollt |
| 2:35pm-3:05pm | Evelyn Lunsain |
| 3:10pm-3:40pm | Daniel Sanz-Alonso |
| 3:45pm-4:15pm | Discussion and closing remarks |
Lightning talks/posters (5 mintues each presentation):
August 21 (Thursday):
Marios Andreou (University of Wisconsin-Madison, Department of Mathematics)
Assimilative Causal Inference
Chuanqi Chen (University of Wisconsin Madison)
Learning Surrogate Models and Efficient Data Assimilation for PDEs via Conditional Gaussian Koopman Network
Catherine Ding (Northwestern University)
Towards Generative Design of Functional Textiles
Mrigank Dhingra (University of Tennessee at Knoxville)
Inverse Problems in Vortex Merger: Learning Governing Dynamics from Partial Observations
Sam Dunham (University of Wisconsin-Madison)
Statistical aspects of constitutive modeling in crystal plasticity and damage mechanics
Louisa Ebby (North Carolina State University)
Wildfire Forecasting from Sparse Observational Measurements
Emmanuel Fleurantin (George Mason University)
ESMDA Applications in Epidemiology and Atmospheric Science
Naureen Ghani (University College London)
TBD
Toan Huynh (Florida State University)
Joint State-Parameter Estimation for the Reduced Fracture Model via the United Filter
Parvathi Kooloth (Pacific Northwest National Laboratory)
Diffusion Models for High Frequency Local Wind Data Assimilation and Prediction
Brian Manuel (North Carolina State University)
TBD
Charlotte Moser (University of Wisconsin-Madison)
Eddy Identification In the Presence of Uncertainty
Jason Louis Torchinsky (Sandia National Laboratories, New Mexico)
Adaptive Radial Basis Function Methods for Data Approximation
August 22 (Friday):
Bipin Tiwari (University of Tennessee at Knoxville)
Comparative Analysis of Advanced Data Assimilation Methods for High-Speed Flow Problems
Shanyin Tong (Columbia University)
Large deviation-informed estimation and control for rare and extreme events
Nathan Waniorek (University of Chicago)
Covariance Operator Estimation and Non-Asymptotic Analysis of the Ensemble Kalman Filter
Zhongrui Wang (University of Wisconsin Madison)
A Closed-Form Nonlinear Data Assimilation Algorithm for Multi-Layer Flow Fields
Nick Wharff (North Carolina State University)
A Deep Neural Network for a Hemiarray EIT System
Xuping Xie (Old Dominion University)
Data Assimilation of Quasi-Geostrophic Turbulent Dynamics
Yinling Zhang (University of Wisconsin Madison)
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization
Pouria Behnoudfar (University of Wisconsin Madison)
Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
Organizers:
Nan Chen (UW-Madison)
Pouria Behnoudfar (UW-Madison)
Jared Whitehead (BYU)
Blake Barker (BYU)