Scientific Machine Learning | SIAM

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2019 SIAM Conference on Com...
Scientific Machine Learning

Scientific Machine Learning

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CSE19 - MS71-1: Projection-based Model Reduction: Formulations for Physics-based Machine Learning

Presentation: Karen E Willcox, University of Texas at Austin, U.S., 22 min 31 sec

CSE19 - MS71-1: Projection-based Model Reduction: Formulations for Physics-based Machine Learning

Document: CSE19 - MS71-1: Projection-based Model Reduction: Formulations for Physics-based Machine Learning

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CSE19 - MS71-2: Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Presentation: Alexander Tartakovsky, Pacific Northwest National Laboratory, U.S., 15 min 16 sec

CSE19 - MS71-2: Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Document: CSE19 - MS71-2: Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

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CSE19 - MS71-4: Machine-learning Error Models for Approximate Solutions to Parameterized Systems of Nonlinear Equations

Presentation: Brian A Freno, Sandia National Laboratories, U.S., 18 min 53 sec

CSE19 - MS71-4: Machine-learning Error Models for Approximate Solutions to Parameterized Systems of Nonlinear Equations

Document: CSE19 - MS71-4: Machine-learning Error Models for Approximate Solutions to Parameterized Systems of Nonlinear Equations