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UID:submissions.supercomputing.org_SC23_sess415@linklings.com
SUMMARY:1st Workshop on Enabling Predictive Science with Optimization and 
 Uncertainty Quantification in HPC
DESCRIPTION:Uncertainty Quantification of Reduced-Precision Time Series in
  Turbulent Channel Flow\n\nWith increased computational power through the 
 use of low-precision arithmetic, a relevant question is how lower precisio
 n affects simulation results, especially for chaotic systems where analyti
 cal round-off estimates are non-trivial to obtain. In this work, we consid
 er how the uncertainty of the t...\n\n\nMartin Karp (KTH Royal Institute o
 f Technology, Sweden); Felix Liu (KTH Royal Institute of Technology, Swede
 n; Raysearch Laboratories); Ronith Stanly (KTH Royal Institute of Technolo
 gy, Sweden); Saleh Rezaeiravesh (University of Manchester); Niclas Jansson
  (KTH Royal Institute of Technology, Sweden); Philipp Schlatter (Friedrich
 -Alexander University, Erlangen-Nuremberg; KTH Royal Institute of Technolo
 gy, Sweden); and Stefano Markidis (KTH Royal Institute of Technology, Swed
 en)\n---------------------\nEPSOUQ-HPC – Morning Break\n------------------
 ---\nUncertainty Quantification of Metal Additive Manufacturing Processing
  Conditions Through the Use of Exascale Computing\n\nMetal additive manufa
 cturing is a disruptive manufacturing technology that opens the design spa
 ce for parts outside those possible from traditional manufacturing methods
 . In order to accelerate industry and R&D needs to certify AM parts, the E
 xaAM project has developed a suite of exascale-ready comp...\n\n\nRobert C
 arson (Lawrence Livermore National Laboratory (LLNL)), Matt Rolchigo and J
 ohn Coleman (Oak Ridge National Laboratory (ORNL)), Mikhail Titov (Brookha
 ven National Laboratory), Jim Belak (Lawrence Livermore National Laborator
 y (LLNL)), and Matt Bement (Oak Ridge National Laboratory (ORNL))\n-------
 --------------\nAutomatic Search Guided Code Optimization Framework for Mi
 xed-Precision Scientific Applications\n\nThe rapid development in machine 
 learning (ML) has prompted demand for low-precision arithmetic hardware th
 at can deliver faster computing speed. Weather simulation applications typ
 ically exhibit higher sensitivity towards small perturbation on the input 
 data, but the inherent uncertainty paves the ...\n\n\nJienan Yao and Wei X
 ue (Tsinghua University, China)\n---------------------\nEfficient Probabil
 istic Tuning of Ensemble Forecasting Method\n\nEnsemble forecasting techni
 ques are gaining popularity in the weather and renewable energy communitie
 s, thanks to their ability to produce accurate predictions and at the same
  time to provide a measure of the uncertainty in the forecast. Analog Ense
 mble techniques are a class of computationally effi...\n\n\nAlessandro Fan
 farillo and Nicholas Malaya (Advanced Micro Devices (AMD) Inc), Guido Cerv
 one (Pennsylvania State University), and Luca Delle Monache (Scripps Resea
 rch Institute)\n---------------------\nWelcome and Introduction\n\nAntigon
 i Georgiadou (Oak Ridge National Laboratory (ORNL)) and Tiernan Casey (San
 dia National Laboratories)\n---------------------\nKeynote Speaker\n\nPete
 r Coveney (University College London)\n---------------------\nOptimized Un
 certainty Estimation for Vision Transformers: Enhancing Adversarial Robust
 ness and Performance Using Selective Classification\n\nDeep Learning model
 s frequently produce high-confidence softmax outputs for out-of-distributi
 on (OOD) inputs, which would ideally be classified as "I don't know". To e
 nhance our model's trustworthiness, we incorporate selective classificatio
 n, which entails abstaining from predictions in situations ...\n\n\nErik P
 autsch (Loyola University, Chicago); John LI (University of California San
  Diego, Argonne National Laboratory (ANL)); Silvio Rizzi (Argonne National
  Laboratory (ANL)); George Thiruvathukal (Loyola University, Chicago); and
  Maria Pantoja (California Polytechnic State University, San Luis Obispo)\
 n---------------------\nLocalization of Gamma-Ray Bursts in a Balloon-Born
 e Telescope\n\nMulti-messenger astrophysics combines observations from mul
 tiple instruments to study transient astrophysical phenomena, many occurri
 ng at seconds-level timescales. To identify and precisely localize these e
 vents in the sky, current systems often search through extensive sensor da
 ta, requiring resou...\n\n\nYe Htet (Washington University in Saint Louis)
 \n---------------------\nClosing Remarks\n\nAntigoni Georgiadou (Oak Ridge
  National Laboratory (ORNL)) and Tiernan Casey (Sandia National Laboratori
 es)\n\nTag: Performance Optimization\n\nRegistration Category: Workshop Re
 g Pass\n\nSession Chairs: Tiernan Casey (Sandia National Laboratories) and
  Antigoni Georgiadou (Oak Ridge National Laboratory (ORNL))
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