The principle of prediction of complex time-dependent nonlinear problems using RNN

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43967733" target="_blank" >RIV/49777513:23220/22:43967733 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9919650" target="_blank" >https://ieeexplore.ieee.org/document/9919650</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CPEE56060.2022.9919650" target="_blank" >10.1109/CPEE56060.2022.9919650</a>

Result language
angličtina
Original language name
The principle of prediction of complex time-dependent nonlinear problems using RNN
Original language description
An approach based on recurrent neural networks (RNNs) is applied to verify the possibility of using surrogate models for the prediction of dynamic nonlinear problems. Modeling complex time dependencies is currently still a challenge, when the structure of the neural network needs to be adapted to the dynamics of the problem. In this paper, the possibility of using prediction in space-time problems is illustrated by the possibility of using it to predict the course of the current in a simple RL circuit that is powered by a voltage source.
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 23rd International Conference on Computational Problems of Electrical Engineering, CPEE 2022
ISBN
979-8-3503-9625-6
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Zuberec,Slovenská republika
Event date
Sep 11, 2022
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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