A paradigm for data-driven predictive modeling using field inversion and machine learning

@article{Parish2016APF,
  title={A paradigm for data-driven predictive modeling using field inversion and machine learning},
  author={Eric J. Parish and Karthik Duraisamy},
  journal={J. Comput. Physics},
  year={2016},
  volume={305},
  pages={758-774}
}
s of talks follow (in order of presentation) List of posters (listed alphabetically) Data-Driven Sampling and Prediction on Manifolds Roger Ghanem1 and Christian Soize2 1University of Southern California, 2Université Paris-Est With the possibility of interpreting data using increasingly complex models we are often faced with the need to embed the data in an ambient space consistent with the parameterization of these models typically a high-dimensional Euclidean space. Constructing probability… CONTINUE READING
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