English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present an approach for constructing a surrogate from ensembles ofinformation sources of varying cost and accuracy. The multifidelity surrogateencodes connections between information sources as a directed acyclic graph,and is trained via gradient-based minimization of a nonlinear least squaresobjective. While the vast majority of state-of-the-art assumes hierarchicalconnections between information sources, our approach works with flexiblystructured information sources that may not admit a strict hierarchy. Theformulation has two advantages: (1) increased data efficiency due toparsimonious multifidelity networks that can be tailored to the application;and (2) no constraints on the training data -- we can combine noisy, non-nestedevaluations of the information sources. Numerical examples ranging fromsynthetic to physics-based computational mechanics simulations indicate theerror in our approach can be orders-of-magnitude smaller, particularly in thelow-data regime, than single-fidelity and hierarchical multifidelityapproaches.

MFNets: Data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources