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ORCIDSergey V. Dolgov
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2020 – today
- 2024
[j30]Sergey Dolgov, Dmitry V. Savostyanov
:
Tensor product approach to modelling epidemics on networks. Appl. Math. Comput. 460: 128290 (2024)- [j29]Tiangang Cui, Sergey Dolgov, Robert Scheichl:
Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events. SIAM J. Sci. Comput. 46(1): 1- (2024) - [i21]Sergey Dolgov, Dmitry V. Savostyanov:
Tensor product algorithms for inference of contact network from epidemiological data. CoRR abs/2401.15031 (2024) - [i20]V. Abronin, A. Naumov, D. Mazur, D. Bystrov, K. Tsarova, Artem Melnikov, Ivan V. Oseledets, Sergey Dolgov, R. Brasher, Michael Perelshtein:
TQCompressor: improving tensor decomposition methods in neural networks via permutations. CoRR abs/2401.16367 (2024) - 2023
- [j28]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. J. Comput. Phys. 485: 112103 (2023) - [j27]Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner:
Efficient Structure-preserving Support Tensor Train Machine. J. Mach. Learn. Res. 24: 4:1-4:22 (2023) - [j26]Harbir Antil, Sergey Dolgov, Akwum Onwunta:
TTRISK: Tensor train decomposition algorithm for risk averse optimization. Numer. Linear Algebra Appl. 30(3) (2023) - [j25]Sergey Dolgov, Dante Kalise, Luca Saluzzi:
Data-Driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations. SIAM J. Sci. Comput. 45(5): 2153- (2023) - [i19]Harbir Antil, Sergey Dolgov, Akwum Onwunta:
State-constrained Optimization Problems under Uncertainty: A Tensor Train Approach. CoRR abs/2301.08684 (2023) - [i18]Kirandeep Kour, Sergey Dolgov, Peter Benner, Martin Stoll, Max Pfeffer:
A weighted subspace exponential kernel for support tensor machines. CoRR abs/2302.08134 (2023) - [i17]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Self-reinforced polynomial approximation methods for concentrated probability densities. CoRR abs/2303.02554 (2023) - [i16]Egor Kornev, Sergey Dolgov, Karan Pinto, Markus Pflitsch, Michael Perelshtein, Artem Melnikov:
Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method. CoRR abs/2305.10784 (2023) - [i15]Sergey Dolgov, Dante Kalise, Luca Saluzzi:
Statistical Proper Orthogonal Decomposition for model reduction in feedback control. CoRR abs/2311.16332 (2023) - 2022
- [j24]Tiangang Cui, Sergey Dolgov:
Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports. Found. Comput. Math. 22(6): 1863-1922 (2022) - [j23]Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl:
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. SIAM/ASA J. Uncertain. Quantification 10(1): 1191-1224 (2022) - [j22]Nikita Gourianov, Michael Lubasch, Sergey Dolgov, Quincy Y. van den Berg, Hessam Babaee, Peyman Givi, Martin Kiffner, Dieter Jaksch:
A quantum-inspired approach to exploit turbulence structures. Nat. Comput. Sci. 2(1): 30-37 (2022) - [i14]Sergey Dolgov, Dante Kalise, Luca Saluzzi:
Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control. CoRR abs/2202.11801 (2022) - [i13]Sergey Dolgov, Dante Kalise, Luca Saluzzi:
Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations. CoRR abs/2205.05109 (2022) - [i12]Oliver Townsend, Silvia Gazzola, Sergey Dolgov, Paul Quinn:
Undersampling Raster Scans in Spectromicroscopy for reduced dose and faster measurements. CoRR abs/2208.09470 (2022) - [i11]Tiangang Cui, Sergey Dolgov, Robert Scheichl:
Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation. CoRR abs/2209.01941 (2022) - [i10]Sergey V. Dolgov, Dmitry V. Savostyanov:
Tensor product approach to modelling epidemics on networks. CoRR abs/2209.03756 (2022) - 2021
- [j21]Sergey Dolgov, Dante Kalise, Karl Kunisch:
Tensor Decomposition Methods for High-dimensional Hamilton-Jacobi-Bellman Equations. SIAM J. Sci. Comput. 43(3): A1625-A1650 (2021) - [j20]Sergey Dolgov, Daniel Kressner, Christoph Strössner:
Functional Tucker Approximation Using Chebyshev Interpolation. SIAM J. Sci. Comput. 43(3): A2190-A2210 (2021) - [i9]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Conditional Deep Inverse Rosenblatt Transports. CoRR abs/2106.04170 (2021) - [i8]Harbir Antil, Sergey Dolgov, Akwum Onwunta:
TTRISK: Tensor Train Decomposition Algorithm for Risk Averse Optimization. CoRR abs/2111.05180 (2021) - 2020
- [j19]Sergey Dolgov, Dmitry V. Savostyanov:
Parallel cross interpolation for high-precision calculation of high-dimensional integrals. Comput. Phys. Commun. 246 (2020) - [j18]Sergey Dolgov, Karim Anaya-Izquierdo, Colin Fox, Robert Scheichl:
Approximation and sampling of multivariate probability distributions in the tensor train decomposition. Stat. Comput. 30(3): 603-625 (2020) - [j17]Alexandra Bünger, Sergey Dolgov, Martin Stoll:
A Low-Rank Tensor Method for PDE-Constrained Optimization with Isogeometric Analysis. SIAM J. Sci. Comput. 42(1): A140-A161 (2020) - [i7]Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl:
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. CoRR abs/2001.08187 (2020) - [i6]Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner:
Efficient Structure-preserving Support Tensor Train Machine. CoRR abs/2002.05079 (2020) - [i5]Tiangang Cui, Sergey Dolgov:
Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports. CoRR abs/2007.06968 (2020) - [i4]Sergey Dolgov, Daniel Kressner, Christoph Strössner:
Functional Tucker approximation using Chebyshev interpolation. CoRR abs/2007.16126 (2020)
2010 – 2019
- 2019
- [j16]Sergey V. Dolgov:
A Tensor Decomposition Algorithm for Large ODEs with Conservation Laws. Comput. Methods Appl. Math. 19(1): 23-38 (2019) - [j15]Sergey Dolgov, Robert Scheichl:
A Hybrid Alternating Least Squares-TT-Cross Algorithm for Parametric PDEs. SIAM/ASA J. Uncertain. Quantification 7(1): 260-291 (2019) - [j14]Sergey Dolgov, John W. Pearson:
Preconditioners and Tensor Product Solvers for Optimal Control Problems from Chemotaxis. SIAM J. Sci. Comput. 41(6): B1228-B1253 (2019) - [i3]Sergey Dolgov, Dante Kalise, Karl Kunisch:
A Tensor Decomposition Approach for High-Dimensional Hamilton-Jacobi-Bellman Equations. CoRR abs/1908.01533 (2019) - [i2]Tobias Breiten, Sergey Dolgov, Martin Stoll:
Solving differential Riccati equations: A nonlinear space-time method using tensor trains. CoRR abs/1912.06944 (2019) - 2018
- [j13]Sergey Dolgov, Vladimir A. Kazeev, Boris N. Khoromskij:
Direct tensor-product solution of one-dimensional elliptic equations with parameter-dependent coefficients. Math. Comput. Simul. 145: 136-155 (2018) - 2017
- [j12]Peter Benner, Sergey Dolgov, Venera Khoromskaia, Boris N. Khoromskij:
Fast iterative solution of the Bethe-Salpeter eigenvalue problem using low-rank and QTT tensor approximation. J. Comput. Phys. 334: 221-239 (2017) - [j11]Sergey Dolgov, Martin Stoll:
Low-Rank Solution to an Optimization Problem Constrained by the Navier-Stokes Equations. SIAM J. Sci. Comput. 39(1) (2017) - 2016
- [j10]Sergey Dolgov, John W. Pearson, Dmitry V. Savostyanov, Martin Stoll:
Fast tensor product solvers for optimization problems with fractional differential equations as constraints. Appl. Math. Comput. 273: 604-623 (2016) - [j9]Simon Etter, Sergey Dolgov, Boris N. Khoromskij:
Erratum: Two-Level QTT-Tucker Format for Optimized Tensor Calculus. SIAM J. Matrix Anal. Appl. 37(2): 818-822 (2016) - 2015
- [j8]Sergey Dolgov, Boris N. Khoromskij, Alexander Litvinenko, Hermann G. Matthies:
Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format. SIAM/ASA J. Uncertain. Quantification 3(1): 1109-1135 (2015) - [j7]Sergey Dolgov, Boris N. Khoromskij:
Simultaneous state-time approximation of the chemical master equation using tensor product formats. Numer. Linear Algebra Appl. 22(2): 197-219 (2015) - 2014
- [j6]Sergey V. Dolgov, Boris N. Khoromskij, Ivan V. Oseledets, Dmitry V. Savostyanov:
Computation of extreme eigenvalues in higher dimensions using block tensor train format. Comput. Phys. Commun. 185(4): 1207-1216 (2014) - [j5]Sergey V. Dolgov, Alexander P. Smirnov, E. E. Tyrtyshnikov:
Low-rank approximation in the numerical modeling of the Farley-Buneman instability in ionospheric plasma. J. Comput. Phys. 263: 268-282 (2014) - [j4]Sergey Dolgov, Dmitry V. Savostyanov:
Alternating Minimal Energy Methods for Linear Systems in Higher Dimensions. SIAM J. Sci. Comput. 36(5) (2014) - 2013
- [j3]Sergey Dolgov, Boris N. Khoromskij:
Two-Level QTT-Tucker Format for Optimized Tensor Calculus. SIAM J. Matrix Anal. Appl. 34(2): 593-623 (2013) - [c1]Sergey Dolgov, Dmitry V. Savostyanov:
Corrected One-Site Density Matrix Renormalization Group and Alternating Minimal Energy Algorithm. ENUMATH 2013: 335-343 - [i1]Sergey V. Dolgov, Alexander P. Smirnov, E. E. Tyrtyshnikov:
Low-rank approximation in the numerical modeling of the Farley-Buneman instability in ionospheric plasma. CoRR abs/1308.5952 (2013) - 2012
- [j2]Ivan V. Oseledets, Sergey V. Dolgov:
Solution of Linear Systems and Matrix Inversion in the TT-Format. SIAM J. Sci. Comput. 34(5) (2012) - [j1]Sergey V. Dolgov, Boris N. Khoromskij, Ivan V. Oseledets:
Fast Solution of Parabolic Problems in the Tensor Train/Quantized Tensor Train Format with Initial Application to the Fokker-Planck Equation. SIAM J. Sci. Comput. 34(6) (2012)
Coauthor Index
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last updated on 2024-06-10 20:30 CEST by the dblp team
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