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.
Original language | English |
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Title of host publication | UNCECOMP 2023: 5th International Conference on Uncertainty Quantification in Computational Science and Engineering |
Subtitle of host publication | Proceedings |
Editors | M. Papadrakakis, V. Papadopoulos, G. Stefanou |
Place of Publication | Athens |
Pages | 745-754 |
Number of pages | 10 |
Publication status | Published - 24 Oct 2023 |
Event | 5th International Conference on Uncertainty Quantification in Computational Science and Engineering - Athens, Greece Duration: 12 Jun 2023 → 14 Jun 2023 https://2023.uncecomp.org/ |
Conference
Conference | 5th International Conference on Uncertainty Quantification in Computational Science and Engineering |
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Abbreviated title | UNCECOMP 2023 |
Country/Territory | Greece |
City | Athens |
Period | 12/06/23 → 14/06/23 |
Internet address |
Keywords
- missing data
- uncertainty quantification
- LSTM
- variational inference (VI)