Projects per year
Personal profile
Research interests
Please use my actual web page https://people.bath.ac.uk/sd901/ instead.
Numerical analysis, linear and multilinear algebra, matrix and tensor analysis, fast algorithms, tensor product formats, dynamical problems, stochastic models
Education/Academic qualification
Mathematics, Doctor rerum naturalium, University of Leipzig
30 Jun 2011 → 20 Aug 2014
Award Date: 20 Aug 2014
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Dive into the research topics where Sergey Dolgov is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Overcoming the curse of dimensionality in dynamic programming by tensor decompositions
Engineering and Physical Sciences Research Council
10/05/21 → 9/05/23
Project: Research council
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Tensor decomposition sampling algorithms for Bayesian inverse problems
Engineering and Physical Sciences Research Council
1/03/21 → 28/02/24
Project: Research council
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Tensor product numerical methods for high-dimensional problems in probability and quantum calculations
1/01/16 → 31/12/18
Project: Research council
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Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports
Cui, T. & Dolgov, S., 21 Sep 2021, In: Foundations of Computational Mathematics. 47 p.Research output: Contribution to journal › Article
Open Access -
Expanding the Range of Hierarchical Equations of Motion by Tensor-Train Implementation
Borrelli, R. & Dolgov, S., 27 May 2021, In: Journal of Physical Chemistry B. 125, 20, p. 5397-5407 11 p.Research output: Contribution to journal › Article › peer-review
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Functional Tucker approximation using Chebyshev interpolation
Dolgov, S., Kressner, D. & Strössner, C., 2021, In: SIAM Journal on Scientific Computing. 43, 3, p. A2190-A2210Research output: Contribution to journal › Article › peer-review
Open Access -
Grid methods for Bayes-optimal continuous-discrete filtering and utilizing a functional tensor train representation
Fox, C., Dolgov, S., Morrison, M. E. K. & Molteno, T. C. A., 6 Jan 2021, (E-pub ahead of print) In: Inverse Problems in Science and Engineering.Research output: Contribution to journal › Article › peer-review
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Tensor decomposition methods for high-dimensional Hamilton-Jacobi-Bellman equations
Dolgov, S., Kalise, D. & Kunisch, K. K., May 2021, In: SIAM Journal on Scientific Computing. 43, 3, p. A1625-A1650 26 p.Research output: Contribution to journal › Article › peer-review
Open Access