Haik et al 2021a - Revision history

Scipediacontent at 12:47, 7 June 2021

2021-06-07T12:47:17Z

Scipediacontent: Scipediacontent moved page Draft Content 575977850 to Haik et al 2021a

2021-06-07T12:35:47Z

Scipediacontent moved page Draft Content 575977850 to Haik et al 2021a

Scipediacontent: Created page with "== Abstract == The Parameterized Background Data-Weak (PBDW) method is a non-intrusive, reduced basis, real-time and in-situ data assimilation framework for physical systems..."

2021-06-07T10:47:24Z

Created page with "== Abstract == The Parameterized Background Data-Weak (PBDW) method is a non-intrusive, reduced basis, real-time and in-situ data assimilation framework for physical systems..."

New page

== Abstract ==

The Parameterized Background Data-Weak (PBDW) method is a non-intrusive, reduced basis, real-time and in-situ data assimilation framework for physical systems modeled by parametrized Partial Differential Equations for steady-state problems. The key idea of the formulation is to seek an approximation to the true field employing projection-by-data, with a first contribution from a deduced background estimate from reduced modeling and a second contribution from an update state informed by the experimental observations (correction of model bias). The present study aims at extending the PBDW formulation for time-dependent problems and proposes a sequential version to deal with sampled data. The work focuses on a time integration in the reduced order model, a data-driven empirical enrichment of the model and a state prediction for future time steps.

== Video ==
{{#evt:service=cloudfront|id=257139|alignment=center|filename=69.mp4}}

== Document ==
<pdf>Media:Draft_Content_575977850A_IDC6_69.pdf</pdf>

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-== Abstract ==
+== Summary ==
 The Parameterized Background Data-Weak (PBDW) method is a non-intrusive, reduced basis, real-time and in-situ data assimilation framework for physical systems modeled by parametrized Partial Differential Equations for steady-state problems. The key idea of the formulation is to seek an approximation to the true field employing projection-by-data, with a first contribution from a deduced background estimate from reduced modeling and a second contribution from an update state informed by the experimental observations (correction of model bias). The present study aims at extending the PBDW formulation for time-dependent problems and proposes a sequential version to deal with sampled data. The work focuses on a time integration in the reduced order model, a data-driven empirical enrichment of the model and a state prediction for future time steps.
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 == Video ==
 {{#evt:service=cloudfront|id=257139|alignment=center|filename=69.mp4}}

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