Efficient Solution of Stochastic Galerkin Matrix Equations via Reduced Basis and Tensor Train Approximation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56208-2_20
Reference10 articles.
1. Béreš, M.: A comparison of approaches for the construction of reduced basis for stochastic Galerkin matrix equations. Appl. Math. 65(2), 191–225 (2020). https://doi.org/10.21136/AM.2020.0257-19
2. Béreš, M.: Methods for the solution of differential equations with uncertainties in parameters. Ph.D. thesis, VŠB-TUO, Ostrava, Czech Republic (2023)
3. Chen, P., Quarteroni, A., Rozza, G.: Comparison between reduced basis and stochastic collocation methods for elliptic problems. J. Sci. Comput. 59(1), 187–216 (2014). https://doi.org/10.1007/s10915-013-9764-2
4. Dolgov, S.V., Savostyanov, D.V.: Alternating minimal energy methods for linear systems in higher dimensions. SIAM J. Sci. Comput. 36(5), A2248–A2271 (2014). https://doi.org/10.1137/140953289
5. Ghanem, R.G., Spanos, P.D.: Stochastic Finite Elements: A Spectral Approach. Springer, Heidelberg (1991). https://doi.org/10.1007/978-1-4612-3094-6
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