Description

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A digital twin (DT) is a computational model that evolves over time to persistently represent the structure, behavior, and context of a unique physical system or process. DTs are characterized by a dynamic and continuous two-way flow of information between the computational model and the physical system. Data streams from the physical system are assimilated into the computational model to reduce uncertainties and improve predictions of the model, which in turn is used as a basis for controlling the physical system, optimizing data acquisition, and providing decision support. The DT must execute rapidly enough to support decisions and controls in time scales relevant to the physical system, and must manage and quantify uncertainties across its lifecycle. Interest in the DT paradigm is growing rapidly across a range of application areas as a way to construct, manage, and capitalize on state-of-the-art computational models, data-driven learning, and decision making under uncertainty for many complex engineered and natural systems. Indeed, the global market for DTs in industry alone is projected to grow to $156 billion by 2030.

This workshop will focus on mathematical, statistical, and computational foundations underlying DTs, in particular addressing challenges in (1) data assimilation and statistical inverse problems, (2) optimal control and decision making, (3) optimal experimental design, and (4) model reduction and surrogates, all in the context of DTs of complex systems. While these fields are “classical,” fundamental challenges arise in the DT setting due to the tight, dynamic interplay between assimilation and control/decisions, the rapid time scales needed for response, the need for predictivity of reduced models/surrogates over control and parameter spaces, and the need for robustness to data and model uncertainties to support high-consequence decisions. These present frontier mathematical, statistical, and computational challenges. The workshop will feature talks and discussion on the four foundational areas identified above, and on the integration of these to address complex applications of DTs to scientific, engineering, medical, and societal problems.

Organizers

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O G
Omar Ghattas The University of Texas, Austin
Y M
Youssef Marzouk Massachusetts Institute of Technology
C S
Claudia Schillings Freie Universität Berlin
I T
Irina Tezaur Sandia National Laboratories

Speakers

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M B
Marc Bocquet Ecole des Ponts ParisTech
M H
Matthias Heinkenschloss Rice University
X H
Xun Huan University of Michigan
J L
Jonas Latz University of Manchester
B P
Benjamin Peherstorfer New York University
S R
Sebastian Reich Universität Potsdam
J R
Johannes Royset University of Southern California
D S
Daniel Sanz-Alonso University of Chicago
K V
Karen Veroy-Grepl Eindhoven University of Technology
Y Y
Yunan Yang Cornell University

Schedule

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Wednesday, June 26, 2024
9:00-9:15 CDT
Welcome Introduction
9:15-10:00 CDT
Leveraging deep learning for geophysical data assimilation and surrogate models

Speaker: Marc Bocquet (Ecole des Ponts ParisTech)

10:00-10:15 CDT
Q&A
10:15-10:45 CDT
Coffee Break
10:45-11:30 CDT
Quasi-Monte Carlo for Bayesian design of experiment problems governed by parametric PDEs

Speaker: Claudia Schillings, (Freie Universität Berlin)

11:30-11:45 CDT
Q&A
11:45-13:00 CDT
Lunch Break
13:00-13:45 CDT
Structured Covariance Operator Estimation in Ensemble Kalman Methods

Speaker: Daniel Sanz-Alonso (University of Chicago)

13:45-14:00 CDT
Q&A
14:00-14:05 CDT
Tech Break
14:05-14:50 CDT
Sequential optimal experimental design for digital twins

Speaker: Xun Huan (University of Michigan)

14:50-15:05 CDT
Q&A
15:05-15:30 CDT
Coffee Break
15:30-16:15 CDT
Measure-Theoretic Approaches for Stochastic Inverse Problem

Speaker: Yunan Yang (Cornell University)

16:15-16:30 CDT
Q&A
Thursday, June 27, 2024
9:00-9:45 CDT
Adaptive surrogate modeling for simulation and optimization of dynamical systems with model inexactness

Speaker: Matthias Heinkenschloss (Rice University)

9:45-10:00 CDT
Q&A
10:00-10:30 CDT
Coffee Break
10:30-11:15 CDT
Advances, opportunities, and challenges for parametric model order reduction in digital twins

Speaker: Karen Veroy-Grepl (Eindhoven University of Technology)

11:15-11:30 CDT
Q&A
11:30-12:45 CDT
Lunch Break
12:45-13:30 CDT
Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations

Speaker: Benjamin Peherstorfer (New York University)

13:30-13:45 CDT
Q&A
13:45-13:50 CDT
Tech Break
13:50-14:35 CDT
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds

Speaker: Jonas Latz (University of Manchester)

14:35-14:50 CDT
Q&A
15:00-16:30 CDT
Social Hour
Friday, June 28, 2024
9:00-9:45 CDT
Stochastic optimal control through a data assimilation lens

Speaker: Sebastian Reich (Universität Potsdam)

9:45-10:00 CDT
Q&A
10:00-10:30 CDT
Coffee Break
10:30-11:15 CDT
Optimization under Stochastic Ambiguity: Optimistic and Pessimistic Perspectives.

Speaker: Johannes Royset (University of Southern California)

11:15-11:30 CDT
Q&A
11:30-12:00 CDT
Workshop wrap-up and open discussion
12:00-12:15 CDT
Time for Workshop survey to be completed on-site

Registration

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