Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Ecomas Proceedia UNCECOMP 2023 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023 - Athens, Griechenland Dauer: 12 Juni 2023 → 14 Juni 2023 |
Publikationsreihe
| Name | International Conference on Uncertainty Quantification in Computational Science and Engineering |
|---|---|
| Band | 5 |
| ISSN (Print) | 2623-3339 |
Abstract
Stochastic processes are widely adopted in many domains to deal with problems which are stochastic in nature and involve strong nonlinearity, nonstationarity and uncertain system parameters. However, the uncertainties of spectral representation of the underlying stochastic processes have not been adequately acknowledged due to the data problems in practice, for instance, missing data. Therefore, this paper proposes a novel method for uncertainty quantification of spectral representation in the presence of missing data using Bayesian deep learning models. A range of missing levels are tested. An example in stochastic dynamics is employed for illustration.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Angewandte Informatik
- Mathematik (insg.)
- Modellierung und Simulation
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Diskrete Mathematik und Kombinatorik
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Ecomas Proceedia UNCECOMP 2023. 2023. (International Conference on Uncertainty Quantification in Computational Science and Engineering; Band 5).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Spectral Density Estimation Of Stochastic Processes Under Missing Data And Uncertainty Quantification With Bayesian Deep Learning
AU - Chen, Yu
AU - Patelli, Edoardo
AU - Edwards, Benjamin
AU - Beer, Michael
N1 - Funding Information: This work was supported by the EU Horizon 2020 - Marie Skłodowska-Curie Actions project URBASIS [Project no. 813137];
PY - 2023
Y1 - 2023
N2 - Stochastic processes are widely adopted in many domains to deal with problems which are stochastic in nature and involve strong nonlinearity, nonstationarity and uncertain system parameters. However, the uncertainties of spectral representation of the underlying stochastic processes have not been adequately acknowledged due to the data problems in practice, for instance, missing data. Therefore, this paper proposes a novel method for uncertainty quantification of spectral representation in the presence of missing data using Bayesian deep learning models. A range of missing levels are tested. An example in stochastic dynamics is employed for illustration.
AB - Stochastic processes are widely adopted in many domains to deal with problems which are stochastic in nature and involve strong nonlinearity, nonstationarity and uncertain system parameters. However, the uncertainties of spectral representation of the underlying stochastic processes have not been adequately acknowledged due to the data problems in practice, for instance, missing data. Therefore, this paper proposes a novel method for uncertainty quantification of spectral representation in the presence of missing data using Bayesian deep learning models. A range of missing levels are tested. An example in stochastic dynamics is employed for illustration.
KW - Evolutionary power spectral density
KW - LSTM
KW - Missing data
KW - Stochastic Variational inference
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85175854631&partnerID=8YFLogxK
U2 - 10.7712/120223.10371.19949
DO - 10.7712/120223.10371.19949
M3 - Contribution to book/anthology
AN - SCOPUS:85175854631
T3 - International Conference on Uncertainty Quantification in Computational Science and Engineering
BT - Ecomas Proceedia UNCECOMP 2023
T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023
Y2 - 12 June 2023 through 14 June 2023
ER -