| Time | Details | |
|---|---|---|
Tuesday, April 26, 2022 | ||
| 10:30 - 10:45 a.m. | Welcome Remarks: Doug Kothe, Oak Ridge National Laboratory | |
| 10:45 - 11:30 a.m. | Panel Discussion | |
| 11:30 - 12:00 p.m. | Invited Speaker 1-1: Saiph Savage, Northeastern University The Future of A.I. for Social Good | |
| 12:00 - 1:00 p.m. | Working Lunch | |
| 1:00 - 2:00 p.m. | Keynote Speaker: Vipin Kumar, University of Minnesota Knowledge-Guided Machine Learning: A New | |
| 2:00 - 2:15 p.m. | Speaker 1-1: Farinaz Koushanfar, University of California San Diego Holistic Co-Design and Optimization of Robust AI | |
| 2:15 - 2:30 p.m. | Speaker 1-2: Guannan Zhang, Oak Ridge National Laboratory Level Set Learning with Pseudo-Reversible Neural Networks for Dimension Reduction in Building High-Dimensional Twins | |
| 2:30 - 2:45 p.m. | Speaker 1-3: Ravi Patel, Sandia National Laboratories Error-in-Variables Modeling for Operator Learning | |
| 2:45 - 3:15 p.m. | Break | |
| 3:15 - 4:15 p.m. | Flash Talks | |
| 4:15 - 5:30 p.m. | Breakout Sessions | |
| 5:30 - 7:30 p.m. | Reception for onsite attendees | |
Wednesday, April 27, 2022 | ||
| 10:30 - 10:45 a.m. | Day 2 Welcome and Introduction: David Womble, Oak Ridge National Laboratory | |
| 10:45 - 11:15 a.m. | Invited Speaker 2-2: Christopher Rackauckas, Massachusetts Institute of Technology The Continuing Advances of Differentiable Simulation | |
| 11:15 - 11:45 a.m. | Invited Speaker: Stephan Mandt, University of California, Irvine Deep Latent Variable Models for Sequential Data | |
| 11:45 - 12:00 p.m. | Speaker 2-4: Nathaniel Trask, Sandia National Laboratories Physics-Informed Multimodal ML for High-Throughput Scientific Discovery | |
| 12:00 - 12:15 p.m. | Speaker 2-5: Mehmet Belviranli, Colorado School of Mines Energy-Aware Execution of Neural Network Interference on Multi-Accelerator Heterogeneous SoCs | |
| 12:15 - 1:15 p.m. | Working Lunch: David Stracuzzi, Sandia National Laboratories Trusted Artificial Intelligence Research Campaign | |
| 1:15 - 1:45 p.m. | Invited Speaker 2-4: Paris Perdikaris, University of Pennsylvania Scalable Operator Learning with Quantified Uncertainty | |
| 1:45 - 2:15 p.m. | Invited Speaker 2-5: Carianne Martinez, Sandia National Laboratories How to Train Your Digital Twin: Practical Deep Learning Approaches to Modeling | |
| 2:15 - 2:30 p.m. | Speaker 2-6: Satish Karra, Los Alamos National Laboratory Constraining Machine Learning with Physics-Based Codes | |
| 2:30 - 2:45 p.m. | Speaker 2-7: Joseph Bakarji, University of Washington Constraining Machine Learning Algorithms with Fundamental Theorems to Discover Differential Equations from Data | |
| 2:45 - 3:15 p.m. | Break | |
| 3:15 - 4:15 p.m. | Flash Talks | |
| 4:15 - 5:30 p.m. | Breakout Sessions | |
Thursday, April 28, 2022 | ||
| 10:30 - 10:45 a.m. | Day 3 Welcome and Introduction: Pradeep Ramuhalli, Oak Ridge National Laboratory | |
| 10:45 - 11:15 a.m. | Invited Speaker 3-6: Olga Fink, École Polytechnique Fédérale de Lausanne Hybrid Operational Digital Twins for Complex Systems: Fusing Physics-Based and Deep Learning Algorithms for Fault Diagnostics and Prognostics | |
| 11:15 - 11:45 a.m. | Invited Speaker 3-7: Mihai Anitescu, Argonne National Laboratory Scalable Physics-based Maximum Likelihood Estimation using Hierarchical Matrices | |
| 11:45 - 12:00 p.m. | Speaker 3-8: Kris Villez, Oak Ridge National Laboratory Shape-Constrained Function Fitting as a Way to Satisfy Prior Knowledge | |
| 12:00 - 12:15 p.m. | Speaker 3-9: Ben Hodges, University of Texas at Austin Challenges and Opportunities for AI with Physics-Based Models of River Networks and Urban Flooding | |
| 12:15 - 1:15 p.m. | Working Lunch: Ben Mintz, Oak Ridge National Laboratory An Interconnected Science Ecosystem for Smart Laboratories of the Future | |
| 1:15 - 1:30 p.m. | Speaker 3-10: TBD TBD | |
| 1:30 - 1:45 p.m. | Speaker 3-11: Yucheng Fu, Pacific Northwest National Laboratory Enhanced Physics-Constrained Deep Neural Networks for the Redox Flow Battery Modeling | |
| 1:45 - 2:00 p.m. | Speaker 3-12: Po-Lun Ma, Pacific Northwest National Laboratory Better and Faster AI-Assisted Aerosol-Cloud Processes in Earth System Models | |
| 2:00 - 3:00 p.m. | Breakout Sessions | |
| 3:00 - 3:30 p.m. | Break | |
| 3:30 - 5:00 p.m. | Breakout Session Out-briefs | |
| 5:00 p.m. | Adjourn | |
| 5:30 p.m. | Adjourn | |