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Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world...

Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2488359233

Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations

About this item

Full title

Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2021-02

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

In many mechanistic medical, biological, physical and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs) can make simulations impractically slow. Biological models require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. Machine lear...

Alternative Titles

Full title

Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2488359233

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2488359233

Other Identifiers

E-ISSN

2331-8422

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