Monday, August 12

8:00-8:45am         Registration

8:45-9:00am          Welcome

9:00-10:15am       Session 1, Chaired by Serge Prudhomme

9:00-9:25am        Amanda Howard, Pacific Northwest National Laboratory

MORE OF A GOOD(?) THING: UNCERTAINTY PROPAGATION THROUGH MULTIFIDELITY DEEP
  OPERATOR NETWORKS

9:25-9:50am        Nathaniel Trask, University of Pennsylvania

A DATA-DRIVEN EXTERIOR CALCULUS FOR PROBABILISTIC DIGITAL TWINS

9:50-10:15am      Ramin Bostanabad, University of California, Irvine

GAUSSIAN PROCESSES: FROM TOPOLOGY OPTIMIZATION TO OPERATOR LEARNING

10:15-10:30am    BREAK

10:30-11:45am    Session 2, Chaired by Johann Guilleminot

10:30-10:55am   Marta D’Elia, Pasteur Labs & Stanford University

SCIENTIFIC MACHINE LEARNING IN INDUSTRIAL SETTINGS

10:55-11:20am  Fernando Rochinha, Universidade Federal do Rio de Janeiro

BAYESIAN FULL WAVE INVERSION USING DEEP PRIORS

11:20-11:45am   Habib Najm, Sandia National Laboratories

APPROXIMATE BAYESIAN MODEL CALIBRATION WITH SUMMARY STATISTICS

11:45am-1:15pm LUNCH (on your own)

1:15-2:30pm         Session 3, Chaired by Jian-Xun Wang

1:15-1:40pm        Bahador Bahmani, Johns Hopkins University

RESOLUTION INDEPENDENT NEURAL OPERATOR (RINO)

1:40-2:05pm        Agnimitra Dasgupta, University of Southern California

SOLVING INVERSE PROBLEMS IN MECHANICS USING CONDITIONAL SCORE-BASED DIFFUSION
           MODELS

2:05-2:30pm       Prasanna Balaprakash, Oak Ridge National Laboratory

SCALABLE AUTOMATED DEEP ENSEMBLE FOR UNCERTAINTY QUANTIFICATION IN SCIENTIFIC
  MACHINE LEARNING

2:30-2:45pm       BREAK

2:45-4:00pm         Session 4, Chaired by Alireza Doostan

2:45-3:10pm        Elizabeth Qian, Georgia Institute of Technology

MULTIFIDELITY LINEAR REGRESSION FOR SCIENTIFIC MACHINE LEARNING FROM SCARCE DATA

3:10-3:35pm   Jim Stewart, Sandia National Laboratories

SCIENTIFIC MACHINE LEARNING AT SANDIA: A SPOTLIGHT

3:35-4:00pm     Noah Wade, Naval Research Laboratory

DEVELOPMENT AND EVALUATION OF AN ONLINE WEIGHT-BALANCING TRAINING
  METHODOLOGY TO IMPROVE NEURAL NETWORK TRAINING FOR UNCERTAINTY PROPATION

4:00-6:00pm Poster Session

Tuesday, August 13         

8:00-9:00am        Registration                                                                                                                                                    

9:00-10:15am   Session 5, Chaired by Michael Shields

9:00-9:25am       Ionut Farcas, Virginia Tech

DISTRIBUTED COMPUTING FOR PHYSICS-BASED DATA-DRIVEN REDUCED MODELING AT SCALE

9:25-9:50am        Romit Maulik, Pennsylvania State University & Argonne National Laboratory

   INTERPRETABLE FINE-TUNING AND ERROR INDICATION FOR GRAPH NEURAL NETWORK SURROGATE
        MODELS

9:50-10:15am      Danial Faghihi, University at Buffalo

STRATEGIC DISCOVERY AND RELIABILITY ASSESSMENT OF DEEP LEARNING SURROGATE MODELS    

10:15-10:30am BREAK

10:30-11:45am    Session 6, Chaired by Assad Oberai

10:30-10:55am     Krishna Garikipati, University of Southern California

INFERENCE OF FOKKER-PLANCK EQUATIONS FOR THE DYNAMICS OF POPULATIONS

10:55-11:20am    Jouni Susiluoto, Jet Propulsion Laboratory, California Institute of Technology

UNCERTAINTY QUANTIFICATION FOR PARTIAL FORWARD MODEL EMULATION IN EARTH REMOTE SENSING

11:20-11:45am       Simon Mak, Duke University

LOCAL TRANSFER LEARNING GAUSSIAN PROCESSES FOR COST-EFFICIENT SURROGATE
  MODELING OF EXPENSIVE COMPUTER SIMULATORS

11:45am-1:15pm LUNCH (on your own)

1:15-2:30pm       Session 7, Chaired by Johann Guilleminot

1:15-1:40pm           Audrey Olivier, University of Southern California

EMBEDDING PHYSICS-DRIVEN UNCERTAINTY IN NEURAL NETWORKS WITH ANCHORED ENSEMBLES

1:40-2:05pm      Charbel Farhat, Stanford University

A NONPARAMETRIC PROBABILISTIC APPROACH FOR MODELING AND QUANTIFYING
  MODEL-FORM UNCERTAINTY IN CFD WITH TURBULENCE MODELING

2:05-2:30pm           Teeratorn Kadeethum, Sandia National Laboratories

PROBABILISTIC INTERPRETATION OF IMPROVED NEURAL OPERATORS FOR LARGE-SCALE   GEOLOGICAL CARBON STORAGE

2:30-2:35pm        SHORT BREAK

2:35-3:45pm Industry Panel, Chaired by Serge Prudhomme

Prasanna Balaprakash, Oak Ridge National Laboratory

Marta D'Elia, Pasteur Labs & Stanford University

Panagiotis Tsilifis, GE Vernova Advanced Research

James Warner, NASA Langley Research Center

2:45-4:00pm        BREAK

4:00-5:15pm         Session 8, Chaired by Alireza Doostan

4:00-4:25pm         Chi (April) Tran, University of Colorado Boulder

WEAK-FORM LATENT SPACE DYNAMICS IDENTIFICATION WITH UQ

4:25-4:50pm      Ruda Zhang, University of Houston

STOCHASTIC SUBSPACE VIA PROBABILISTIC PCA TO CHARACTERIZE AND CORRECT MODEL ERROR

4:50-5:15pm           Pan Du, University of Notre Dame

CONFILD: CONDITIONAL NEURAL FIELD LATENT DIFFUSION MODEL GENERATING SPATIOTEMPORAL TURBULENCE

Wednesday, August 14

8:00-9:00am       Registration                                                                                                                                                    

9:00-10:15am   Session 9, Chaired by Jian-Xun Wang

9:00-9:25am        Toryn Schafer, Texas A&M University

MACHINE LEARNING DATA-DRIVEN CLOSURE MODELS

9:25-9:50am       Tan Bui-Thanh, The University of Texas at Austin

LEARN2SOLVE: A MODEL-CONSTRAINED TANGENT APPROACH FOR SUPERSONIC FLOWS

9:50-10:15am      Yannis Kevrekidis, Johns Hopkins University   

ON SAMPLING THE THINGS THAT DO NOT MATTER            

10:15-10:30am BREAK

10:30-11:45am    Session 10, Chaired by Michael Shields

10:30-10:55am      Roger Ghanem, University of Southern California

PHYSICS EXTRACTION PODS (PEP): AN EVOLUTION OF THE RVE

10:55-11:20am    Abani Patra, Tufts University

LANDSLIDES AND ROCKETS: DATA, ML AND UQ FOR COUPLED MULTISCALE PHYSICS

11:20-11:45am      Khachik Sargsyan, Sandia National Laboratories

EMBEDDED FRAMEWORK FOR MODEL ERROR QUANTIFICATION AND PROPAGATION

11:45am-12:45pm Funding Panel, Chaired by Assad Oberai

Yannis Kevrekidis, DARPA

Reza Malek-Madani, ONR

Siddiq Qidwai, NSF

12:45-1:45pm      LUNCH (on your own)