Description

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This workshop addresses the “physical-to-virtual” leg of the Digital Twins (DT) framework, in which observational or experimental data from the physical system is assimilated into a virtual model of its dynamics on a moving time horizon to infer model components (such as  initial conditions boundary conditions, material coefficients, source terms). The focus will be on statistical formulations of data assimilation and inverse problems, notably the Bayesian framework, motivated by the critical need to quantify and manage uncertainty in DTs from data to inference to prediction to decisions. Several major challenges that arise in the DT framework will be addressed: (1) the goal-oriented (“fit-for-purpose”) nature of DTs, implying that the inverse solution needs to be accurate with respect to the downstream prediction/control quantities of interest, as opposed to the full parameter or state fields; (2) the real-time nature of DTs (dictated by the time scales of the dynamical system), necessitating fast algorithms for inference; (3) the fact that DTs typically describe complex physical systems (large-scale, multiphysics, multiscale, multirate) with high dimensional parameter/state spaces (often discretizations of infinite dimensional fields), necessitating scalable dimension-independent formulations and algorithms; (4) the need for inference methods that can exploit the sequential nature of the data assimilation/inverse problems; (5) the forward models often will include stochastic forcings, leading to “intractable” likelihoods that must be dealt with; and (6) the crucial task of accounting for model (structural) uncertainty in Bayesian inference remains an open problem in the general setting.

Poster Session

This workshop will include a poster session for early career researchers (including graduate students). In order to propose a poster, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. The registration form should not be used to propose a poster.

The deadline for proposing is August 31, 2025. If your proposal is accepted, you should plan to attend the event in-person.

Funding

All funding has been allocated for this event.

Organizers

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Y M
Youssef Marzouk MIT
O G
Omar Ghattas University of Texas, Austin
S R
Sebastian Reich University of Postdam
R W
Rebecca Willett University of Chicago

Speakers

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L B
Laura Balzano University of Michigan
R B
Ricardo Baptista Caltech
M B
Marc Bocquet École des Ponts
A B
Amy Braverman Jet Propulsion Laboratory (JPL)
N C
Nisha Chandramoorthy University of Chicago
T C
Tiangang Cui University of Sydney
E H
Eldad Haber University of British Columbia
F H
Felix Herrmann Georgia Tech
B H
Bamdad Hosseini University of Wisconsin
Q L
Qin Li University of Wisconsin
C M
Cecilia Mondaini Drexel
N N
Nicholas Nelsen Cornell University
T O
Thomas O’Leary-Roseberry The Ohio State University
B P
Benjamin Peherstorfer NYU
N P
Noemi Petra UC-Merced
M P
Mirjeta Pasha Virginia Tech
D S
Daniel Sanz-Alonso University of Chicago
W S
Wilhelm Stannat TU Berlin
M T
Margaret Trautner Caltech
P J v L
Peter Jan van Leeuwen Colorado State

Schedule

Monday, October 6, 2025
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:40 CDT
Learning surrogate models and data assimilation processes for advanced geophysical dynamics forecasting

Speaker: Marc Bocquet (École nationale des ponts et chaussées)

9:40-9:50 CDT
Q&A
9:50-10:20 CDT
Coffee Break
10:20-11:00 CDT
Tensor Trains for Sequential State and Parameter Estimation in State-space Models

Speaker: Tiangang Cui (School of Mathematics and Statistics)

11:00-11:10 CDT
Q&A
11:10-11:35 CDT
Break
11:35-12:15 CDT
Filtering SPDEs with Spatio-Temporal Point Process ObservationsI

Speaker: Wilhelm Stannat (Technische Universität Berlin)

12:15-12:25 CDT
Q&A
12:25-13:35 CDT
Lunch Break
13:35-14:15 CDT
TBA

Speaker: Nisha Chandramoorthy (University of Chicago)

14:15-14:25 CDT
Q&A
14:25-14:40 CDT
Break
14:40-15:30 CDT
Lightning Talks
15:30-17:00 CDT
Social Hour/Poster Session
Tuesday, October 7, 2025
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:40 CDT
A Mathematical Perspective On Contrastive Learning

Speaker: Ricardo Baptista (University of Toronto)

9:40-9:50 CDT
Q&A
9:50-10:20 CDT
Coffee Break
10:20-11:00 CDT
TBA

Speaker: Qin Li (University of Wisconsin, Madison)

11:00-11:10 CDT
Q&A
11:10-11:35 CDT
Break
11:35-12:15 CDT
Priorconditioned Sparsity-Promoting Projection Methods for Deterministic and Bayesian Linear Inverse Problems

Speaker: Mirjeta Pasha (Virginia Polytechnic Institute & State University (Virginia Tech))

12:15-12:25 CDT
Q&A
12:25-13:40 CDT
Lunch Break
13:40-14:20 CDT
Exploiting Low-Dimensional Structure in Bayesian Inverse Problems Governed by Ice Sheet Flow Models

Speaker: Noemi Petra (University of California, Merced (UC Merced))

14:20-14:30 CDT
Q&A
14:30-15:00 CDT
Coffee Break
15:00-15:40 CDT
Context-Aware Digital Twin for Underground Storage Operations and Decision Making

Speaker: Felix Herrmann (Georgia Institute of Technology)

15:40-15:50 CDT
Q&A
Wednesday, October 8, 2025
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:40 CDT
Efficient Low-Dimensional Compression for Deep Overparameterized Learning and Fine-Tuning

Speaker: Laura Balzano (University of Michigan)

9:40-9:50 CDT
Q&A
9:50-10:20 CDT
Coffee Break
10:20-11:00 CDT
Investigating Model Discrepancy Using a Simulation-based Framework

Speaker: Amy Braverman (Jet Propulsion Laboratory, California Institute of Technology)

11:00-11:10 CDT
Q&A
11:10-11:35 CDT
Break
11:35-12:15 CDT
Operator Learning for History-Dependent and Multiscale Problems

Speaker: Margaret Trautner (California Institute of Technology)

12:15-12:25 CDT
Q&A
12:25-13:40 CDT
Lunch Break
13:40-14:20 CDT
Data-efficient kernel methods for learning differential equations and their solution operators

Speaker: Bamdad Hosseini (University of Washington)

14:20-14:30 CDT
Q&A
14:30-15:00 CDT
Coffee Break
15:00-16:00 CDT
Roundtable discussion: Challenges and Opportunities in Data Assimilation and Inverse Problems for Digital Twins
Thursday, October 9, 2025
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:40 CDT
Training distribution optimization in the space of probability measures

Speaker: Nicholas Nelsen (Cornell University)

9:40-9:50 CDT
Q&A
9:50-10:20 CDT
Coffee Break
10:20-11:00 CDT
Towards Decision-Ready Operator Surrogates

Speaker: Thomas O'Leary-Roseberry (Ohio State University)

11:00-11:10 CDT
Q&A
11:10-11:35 CDT
Break
11:35-12:15 CDT
Formulating the digital twin problem as a two-step Bayesian Inference problem

Speaker: Peter Jan van Leeuwen (Colorado State University, Fort Collins)

12:15-12:25 CDT
Q&A
12:25-13:40 CDT
Lunch Break
13:40-14:20 CDT
Multiproposal MCMC algorithms and their large proposal limits

Speaker: Cecilia Mondaini (Drexel University)

14:20-14:30 CDT
Q&A
14:30-15:00 CDT
Coffee Break
15:00-15:40 CDT
TBA

Speaker: Eldad Haber (University of British Columbia)

15:40-15:50 CDT
Q&A
Friday, October 10, 2025
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:40 CDT
Transport- and Measure-Theoretic Approaches for Dynamical System Modeling

Speaker: Yunani Yang (Cornell University)

9:40-9:50 CDT
Q&A
9:50-10:20 CDT
Break
10:20-11:00 CDT
 Ensemble Kalman Filters: Long-Time and Small-Ensemble Analyses

Speaker: Daniel San-Alonso (University of Chicago)

11:00-11:10 CDT
Q&A
11:10-11:35 CDT
Coffee Break
11:35-12:15 CDT
DICE: Discrete inverse continuity equation for learning population dynamics

Speaker: Benjamin Peherstorfer (Courant Institute of Mathematical Sciences)

12:15-12:25 CDT
Q&A
12:30-12:45 CDT
Workshop survey and close

Registration

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IMSI is committed to making all of our programs and events inclusive and accessible. Contact [email protected] to request disability-related accommodations.

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