Solving stochastic systems with low-rank tensor compression
By Hermann G. Matthies, Elmar Zander. 2012
88 citations recorded by the source corpus.
Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Alexander Litvinenko
- M. Espig
- Wolfgang Hackbusch
- Hermann G. Matthies
- Elmar Zander
- Christine Tobler
- Lars Grasedyck
- Daniel Kressner
In Statistical and Nonlinear Physics
- Wolfgang Hackbusch
- Alexander Litvinenko
- M. Espig
- Elmar Zander
- Hermann G. Matthies
- Philipp Wähnert
- Anthony Nouy
- Christoph Schwab
In Statistics and Probability
- Wolfgang Hackbusch
- M. Espig
- Alexander Litvinenko
- Elmar Zander
- Hermann G. Matthies
- Philipp Wähnert
- Anthony Nouy
- Christoph Schwab
Citations in the corpus
- A literature survey of low‐rank tensor approximation techniques (2013, neutral): “• the Richardson method combined with QTT [161] and low-rank matrix decompositions [189]; • a projection method combined with HT [16]; • the conjugate gradient method, BiCGStab and other Krylov subspace methods combined with HT [169, 171,246] and low-rank matrix decompositions [32]; • GMRES…”
- Efficient Analysis of High Dimensional Data in Tensor Formats (2012, methodology): “Additional interesting result in [38] is the research of different strategies for the tensor-rank truncation after each iteration.”
- Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats (2014, background): “Keywords: tensor approximation, stochastic PDEs, stochastic Galerkin matrix, lowrank tensor formats, uncertainty quantification AMS Subject Classifications: 65N30, 35R60, 15A69”