Bayesian methods in neural networks for inverse atmospheric modelling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00588431" target="_blank" >RIV/67985556:_____/24:00588431 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Bayesian methods in neural networks for inverse atmospheric modelling

Original language description

Recovering a source and an amount of an emitted substance from distant measurement is an ill-posed problem. In this contribution, two methods based on Bayes theorem will be compared on a realistic toy problem with microplastics. First of them is a Bayesian neural network pretrained to mimic a lognormal process and second one is hierarchical variational model, where the parameters of the posterior distribution are modeled by a convolutional neural network. Both these approaches allow to incorporate spatial dependency of the locations of the source and offer an estimate of uncertainty to assess the reliability of the method.n

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-10400S" target="_blank" >GA24-10400S: Advanced Bayesian methods for estimation of atmospheric pollutant sources</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů