All times are in EDT.


Tuesday, April 18, 2023

7:30 a.m.Badging, Registration and Working Breakfast

8:00 a.m.Pradeep Ramuhalli, Oak Ridge National Laboratory
Welcome and Introduction
9:00 - 11:10 a.m.Technical Session 1: Digital Twin Model Development and Lifecycle Management
Session Chair: Katarzyna Swirydowicz
9:00 a.m.Invited Speaker 1-1: Ronald BoringIdaho National Laboratory
Explainable Natural and Artificial Intelligence: The Human in the Loop
9:30 a.m.Speaker 1-1: Guannan Zhang, Oak Ridge National Laboratory
Transferable Neural Feature Spaces for Partial Differential Equations
9:50 a.m.Speaker 1-2: Kibaek Kim, Argonne National Laboratory
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
10:10 a.m.Break
10:30 - 11:45 a.m.Flash Talks 

Machine Learning-Based Compact Modeling of Multi-State Devices
Amedullah Aziz, University of Tennessee, Knoxville
RESET Artificial Intelligence
Vivek Agarwal, Idaho National Laboratory

Merging Physics with ML for HTGR Plant Component Design and Health Monitoring
Purushotham Balaji, Caterpillar, Inc.

Towards the Development of a Level 5 Digital Twin of an Exascale Supercomputer
Wes Brewer, Oak Ridge National Laboratory

Application of an AI-Based Digital Twin of a Combustion Engine within a Physics-Informed Framework: A Case Study
Brian Kaul, Oak Ridge National Laboratory
 Model-Form Uncertainty for Digital Twins
Teresa Portone, Sandia National Laboratories

Towards Human-Centric Digital Twin of Machine Tool by a System-Driven AI Engineering Approach
Tony Shi, University of Tennessee, Knoxville

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems
Xingang Zhao, Oak Ridge National Laboratory

11:45 a.m.Pradeep Ramuhalli, Oak Ridge National Laboratory
Breakout Session Introductions (Working Lunch)
1:00 - 2:00 p.m.Technical Session 2: Assured Digital Twins
Session Chair: Kishan Rajput
1:00 p.m.Speaker 1-3: Shady Ahmed, Pacific Northwest National Laboratory
Multifidelity Operator Networks for Closure Modeling in Multiscale Systems
1:20 p.m.Speaker 1-4: Arvind Mohan, Los Alamos National Laboratory
Non-Intrusive Machine Learning Models of PDEs with Differentiable Programming: An Interpretability Study
1:40 p.m.Speaker 1-5: Mark Cianciosa, Oak Ridge National Laboratory
Machine Learning Proxy Model Refinement using Interference Uncertainty Estimation
2:00 - 3:15 p.m.Flash Talks

Gaussian Process Port-Hamiltonian Systems
Thomas Beckers, Vanderbilt University

A Sustainable Machine Learning Framework for SNS
Yigit Yucesan, Oak Ridge National Laboratory

Continual Learning for Physical Systems
Panos Stinis, Pacific Northwest National Laboratory

Uncertainty Quantification in Assessing Storm Surges
Pulong Ma, Clemson University

Machine Learning for Joint Performance Prediction
Dali Wang, Oak Ridge National Laboratory

In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Riccardo Balin, Argonne National Laboratory

Learning Multi-Scale Diffusion Fields Using Satellite-Observed Aerosol Trajectories
Lekah Patel, Sandia National Laboratories

3:15 p.m.Break
3:30 p.m.Breakouts
4:45 p.m.Poster Setup
5:00 p.m.Welcome Reception and Poster Session
7:00 p.m.Day 1 Ends

Wednesday, April 19, 2023

7:30 a.m.Badging, Registration, and Breakfast
8:00 a.m.Pradeep Ramuhalli, Oak Ridge National Laboratory
Welcome and Agenda Overview / Introduction
8:30 a.m.Keynote: Lynne Parker, University of Tennessee Knoxville
AI Policies and Opportunites
9:30 a.m.Break
10:00 - 11:30 a.m.Technical Session 3: Sensors and Data Management
Session Chair: Prasanna Balaprakash
10:00 a.m.Invited Speaker 2-2: Rao Kotamarthi, Argonne National Laboratory
Climate and Weather Modeling Using AI
10:30 a.m.Speaker 2-6: Miguel Bessa, Brown University
Cooperative Data-Driven Modeling
10:50 a.m.Speaker 2-7: Rebekah White, Sandia National Laboratory
Optimal Sensor Placement for Maximizing Information Regarding Optimization Objectives
11:10 a.m.Speaker 2-8: Prasanna BalaprakashOak Ridge National Laboratory
Neural Architecture Search for Scientific Machine Learning with Quantified Uncertainty
11:30 a.m.Group Photo and Break
12:00 p.m.Vincent PaquitOak Ridge National Laboratory
A Digital Factory for Advanced Manufacturing Technologies (Working Lunch)
1:00 - 2:30 p.m.Technical Session 4: Assured Digital Twins
Session Chair: Miguel Bessa
1:00 p.m.Invited Speaker 2-3: Petros Koumoutsakos, Harvard University
AI/Scientific Computing: Algorithmic Alloys for Digital Twins
1:30 p.m.Speaker 2-9: Nurali Virani, GE Research

Reliable Anomaly Detection with Individual Prediction Reliability 
1:50 p.m.Speaker 2-10: Chetan KulkarniNASA Ames Research
Hybrid Modeling for Complex Systems Health Management
2:10 p.m.Breakouts
3:00 p.m.Break
4:45 p.m.Day 2 Ends

Thursday, April 20, 2023

7:30 a.m.Badging, Registration, and Breakfast
8:00 a.m.Pradeep Ramuhalli, Oak Ridge National Laboratory
Welcome and Agenda Overview
8:30 - 10:30 a.m.Technical Session 5: General Topics in Digital Twins
Session Chair: Ryan King
8:30 a.m.Invited Speaker 3-4: Pascal Van Hentenryck, Georgia Tech
The Fusion of Machine Learning and Optimization for Engineering
9:00 a.m.Speaker 3-11: William Dawson, Lawrence Livermore National Laboratory
Dynamic Bayesian Network Twins for Robust Collaborative Decision Making
9:20 a.m.Speaker 3-12: Tia Miceli, Fermilab
MLOps for Digital Twins
9:40 a.m.Speaker 3-13: Jay Kapat, University of Central Florida
Digital Twin for a Gas Turbine
10:00 a.m.Speaker 3-14: Jan Drgona, Pacific Northwest National Laboratory
Differentiable Programming for Modeling and Control of Dynamical Systems
10:30 a.m.Break
11:00 a.m.Draguna Vrabie, Pacific Northwest National Laboratory
Nurali Virani, GE Research
Jan Drgona, Pacific Northwest National Laboratory
Gopika Bhardwaj, Fermilab

Breakout Reports (Working Lunch)
1:00 p.m.Workshop Closeout
1:00 - 5:00 p.m.Optional Tours