Higher-Order Neural Networks for Efficient Physics-Informed Solutions of Partial Differential Equations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00386097" target="_blank" >RIV/68407700:21220/25:00386097 - isvavai.cz</a>
Result on the web
<a href="https://compmech.kme.zcu.cz/info_sborniky.php" target="_blank" >https://compmech.kme.zcu.cz/info_sborniky.php</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Higher-Order Neural Networks for Efficient Physics-Informed Solutions of Partial Differential Equations
Original language description
Present contribution deals with an alternative method for solving partial differential equations based on physics-informed neural networks (PINNs). A number of shallow neural networks consisting of standard perceptron network are tested and compared to networks with higher order synaptic operations (HONNs). Viscous Burgers equation is chosen as a representative example. It turns out that HONNs are able to solve the problem with order of magnitude less optimizable parameters and furthermore with higher accuracy of the solution.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PROCEEDINGS OF COMPUTATIONAL MECHANICS 2025
ISBN
978-80-261-1254-9
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
82-85
Publisher name
Západočeská univerzita v Plzni
Place of publication
Plzeň
Event location
Srní
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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