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Multifidelity methods leverage both low- and high-fidelity data in order to maximize the accuracy of model estimates, while minimizing the cost associated with parametrization. They have been successfully used in impedance cardiography, wing-design optimization, robotic learning, and have more recently been extended to human-in-the-loop systems, such as aerospace and transportation. They include both model-based methods, where a generative model is available or can be learned, in addition to model-free methods, that include regression-based approaches, such as stacked-regression. A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression, Gaussian mixture models, Gaussian processes, auto-regressive Gaussian processes, or Bayesia

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rdfs:label	Multifidelity simulation (en)
rdfs:comment	Multifidelity methods leverage both low- and high-fidelity data in order to maximize the accuracy of model estimates, while minimizing the cost associated with parametrization. They have been successfully used in impedance cardiography, wing-design optimization, robotic learning, and have more recently been extended to human-in-the-loop systems, such as aerospace and transportation. They include both model-based methods, where a generative model is available or can be learned, in addition to model-free methods, that include regression-based approaches, such as stacked-regression. A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression, Gaussian mixture models, Gaussian processes, auto-regressive Gaussian processes, or Bayesia (en)
name	Multifidelity simulation methods (en)
foaf:depiction
dct:subject	Data modeling Estimation methods
Wikipage page ID	55958638 (xsd:integer)
Wikipage revision ID	1068795395 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian linear regression Probability distribution Approximation algorithm Judea Pearl Estimation theory Wing-shape optimization Gaussian process Generative model Nonparametric statistics Machine learning Parametric statistics Polynomial chaos Statistical parameter Physical system Systems engineering Distributed Interactive Simulation Data modeling System deployment Regression analysis Risk analysis Estimation methods Aerospace engineering Transport Autoregressive model Fidelity Human-in-the-loop Operations research X-Plane (simulator) Simulation Predictive validity Impedance cardiography List of things named after Thomas Bayes Robot learning Simulation modeling Stochastic process Training data Gaussian mixture model KL divergence
sameAs	Multifidelity simulation Multifidelity simulation
space	Not defined (en)
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:Infobox_algorithm
thumbnail	wiki-commons:Special:FilePath/Mutlifid.jpg?width=300
caption	Multifidelity Simulation Methods for Transportation (en)
class	Simulation (en) (en) Model-based methods (en) Model-free methods (en)
data	Low- and high-fidelity data (en)
time	Not defined (en)
has abstract	Multifidelity methods leverage both low- and high-fidelity data in order to maximize the accuracy of model estimates, while minimizing the cost associated with parametrization. They have been successfully used in impedance cardiography, wing-design optimization, robotic learning, and have more recently been extended to human-in-the-loop systems, such as aerospace and transportation. They include both model-based methods, where a generative model is available or can be learned, in addition to model-free methods, that include regression-based approaches, such as stacked-regression. A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression, Gaussian mixture models, Gaussian processes, auto-regressive Gaussian processes, or Bayesian polynomial chaos expansions. The approach used depends on the domain and properties of the data available, and is similar to the concept of metasynthesis, proposed by Judea Pearl. (en)
prov:wasDerivedFrom	wikipedia-en:Multifidelity_simulation?oldid=1068795395&ns=0
page length (characters) of wiki page	13798 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Multifidelity_simulation
is Link from a Wikipage to another Wikipage of	Data fusion Karen Willcox
is known for of	Karen Willcox
is known for of	Karen Willcox
is foaf:primaryTopic of	wikipedia-en:Multifidelity_simulation

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