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  1. Courses
  2. 2019 SIAM Conference on Com...
  3. Machine Learning in Computa...

Machine Learning in Computational Science

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CSE19 - MS70-1: A Learning-based Approach for Data Assimilation

Presentation: Ahmed Attia, 20 min 6 sec
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CSE19 - MS70-1: A Learning-based Approach for Data Assimilation

Document: CSE19 - MS70-1: A Learning-based Approach for Data Assimilation
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CSE19 - MS70-2: Bayesian Model Selection, Calibration and Uncertainty Quantification for Thermodynamic Properties

Presentation: Noah Paulson, Argonne National Laboratory, U.S., 17 min 50 sec
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CSE19 - MS70-2: Bayesian Model Selection, Calibration and Uncertainty Quantification for Thermodynamic Properties

Document: CSE19 - MS70-2: Bayesian Model Selection, Calibration and Uncertainty Quantification for Thermodynamic Properties
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CSE19 - MS70-3: Inferring Black Box Functions under a Limited Computational Budget

Presentation: Piyush Pandita, Purdue University, U.S., 18 min 57 sec
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CSE19 - MS70-3: Inferring Black Box Functions under a Limited Computational Budget

Document: CSE19 - MS70-3: Inferring Black Box Functions under a Limited Computational Budget
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CSE19 - MS70-4: Statistical Learning Approaches for Automatic Parameter Tuning to Increase Parallel Performance in Large CFD Simulations

Presentation: Zachary Cooper, Virginia Tech, U.S., 23 min 10 sec
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CSE19 - MS70-4: Statistical Learning Approaches for Automatic Parameter Tuning to Increase Parallel Performance in Large CFD Simulations

Document: CSE19 - MS70-4: Statistical Learning Approaches for Automatic Parameter Tuning to Increase Parallel Performance in Large CFD Simulations
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