Numerical Solution and Uncertainty Quantification of Bioheat Transfer Equation Using Neural Network Approach (CROSBI ID 694676)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Lojić Kapetanović, Ante ; Šušnjara, Anna ; Poljak, Dragan
engleski
Numerical Solution and Uncertainty Quantification of Bioheat Transfer Equation Using Neural Network Approach
The paper deals with a novel approach to carry out uncertainty quantification in modelling of bioheat transfer equation using neural networks and deep learning. The output uncertainty is achieved via Monte Carlo (MC) dropout procedure using Bayesian inference, while the input uncertainty propagation is achieved using MC simulation of the ensemble of physics-informed neural networks (PINNs). The proposed approach uses a neural network with integrated physical knowledge without need of any prior assumptions and mesh generation, respectively. Deterministic modelling is related to both analytical and numerical (finite element method) solution of bioheat transfer equation. The computational example considered in this work pertains to the solution of rather simple one-dimensional problem of bioheat transfer, as a kind of opener to the subject.
bioelectromagnetism ; bioheat transfer equation ; numerical modelling ; physics-informed neural networks ; uncertainty quantification
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
1-6.
2020.
objavljeno
10.23919/SpliTech49282.2020.9243733
Podaci o matičnoj publikaciji
Engineering Modelling, SpliTech 2020
Podaci o skupu
5th International Conference on Smart and Sustainable Technologies 2020
predavanje
23.09.2020-26.09.2020
Bol, Hrvatska