Sergey Dolgov

[1]   Self-reinforced polynomial approximation methods for concentrated probability densities. arXiv preprint 2303.02554, 2023. (With T. Cui and O. Zahm)

[2]   Statistical proper orthogonal decomposition for model reduction in feedback control. arXiv preprint 2311.16332, 2023. (With D. Kalise and L. Saluzzi)

[3]   A weighted subspace exponential kernel for support tensor machines. arXiv preprint 2302.08134, 2023. (With K. Kour, P. Benner, M. Stoll and M. Pfeffer)

[4]   Deep importance sampling using Tensor Trains with application to a priori and a posteriori rare events. SIAM Journal on Scientific Computing, 46(1):C1–C29, 2024. (With T. Cui and R. Scheichl)

[5]   Tensor product approach to modelling epidemics on networks. Applied Mathematics and Computation, 460:128290, 2024. (With D. Savostyanov)

[6]   TTRISK: Tensor train decomposition algorithm for risk averse optimization. Numerical Linear Algebra with Applications, 30(3):e2481, 2023. (With H. Antil and A. Onwunta)

[7]   Scalable conditional deep inverse rosenblatt transports using tensor trains and gradient-based dimension reduction. Journal of Computational Physics, 485:112103, 2023. (With T. Cui and O. Zahm)

[8]   Data-driven Tensor Train gradient cross approximation for Hamilton–Jacobi–Bellman equations. SIAM Journal on Scientific Computing, 45(5):A2153–A2184, 2023. (With D. Kalise and L. Saluzzi)

[9]   Efficient structure-preserving support Tensor Train machine. Journal of Machine Learning Research, 24(4):1–22, 2023. (With K. Kour, M. Stoll and P. Benner)

[10]   Deep composition of Tensor-Trains using squared inverse Rosenblatt transports. Found. Comput. Math., 22(6):1863–1922, 2022. (With T. Cui)

[11]   A quantum-inspired approach to exploit turbulence structures. Nature Computational Science, 2(1):30–37, 2022. (With N. Gourianov, M. Lubasch, Q. Y. v. d. Berg, H. Babaee, P. Givi, M. Kiffner and D. Jaksch)

[12]   Rank bounds for approximating Gaussian densities in the Tensor-Train format. SIAM/ASA Journal on Uncertainty Quantification, 10(3):1191–1224, 2022. (With P. B. Rohrbach, L. Grasedyck and R. Scheichl)

[13]   Undersampling raster scans in spectromicroscopy for reduced dose and faster measurements. Optics Express, 30(24):43237–43254, 2022. (With O. Townsend, S. Gazzola and P. Quinn)

[14]   Tensor decomposition methods for high-dimensional Hamilton–Jacobi–Bellman equations. SIAM J. Sci. Comput., 43(3):A1625–A1650, 2021. (With D. Kalise and K. Kunisch)

[15]   Low-rank solution of an optimal control problem constrained by random Navier-Stokes equations. Int. J. Num. Meth. in Fluids, 92(11):1653–1678, 2020. (With P. Benner, A. Onwunta and M. Stoll)

[16]   Approximation and sampling of multivariate probability distributions in the tensor train decomposition. Statistics and Computing, 30:603–625, 2020. (With K. Anaya-Izquierdo, C. Fox and R. Scheichl)

[17]   Parallel cross interpolation for high–precision calculation of high–dimensional integrals. Comp. Phys. Comm., 246:106869, 2020. (With D. Savostyanov)

[18]   Guaranteed a posteriori error bounds for low-rank tensor approximate solutions. IMA J. Num. Analysis, 41(2):1240–1266, 2020. (With T. Vejchodsky)

[19]   Parallel time-dependent variational principle algorithm for matrix product states. Phys. Rev. B, 101:235123, 2020. (With P. Secular, N. Gourianov, M. Lubasch, S. R. Clark and D. Jaksch)

[20]   Preconditioners and tensor product solvers for optimal control problems from chemotaxis. SIAM J. Sci. Comput., 41(6):B1228–B1253, 2019. (With J. W. Pearson)

[21]   A hybrid Alternating Least Squares–TT-Cross algorithm for parametric PDEs. SIAM/ASA Journal on Uncertainty Quantification, 7(1):260–291, 2019. (With R. Scheichl)

[22]   A tensor decomposition algorithm for large ODEs with conservation laws. Computational Methods in Applied Mathematics, 19:23–38, 2019.

[23]   Greedy low-rank algorithm for spatial connectome regression. J. Math. Neurosc., 9:9, 2019. (With P. Kurschner, K. D. Harris and P. Benner)

[24]   Tensor product approach to quantum control. In Integral Methods in Science and Engineering. Birkhauser, 2019. (With D. Quinones-Valles and D. Savostyanov)

[25]   Fast iterative solution of the Bethe-Salpeter eigenvalue problem using low-rank and QTT tensor approximation. J. Comput. Phys., 334:221 – 239, 2017. (With P. Benner, V. Khoromskaia and B. N. Khoromskij)

[26]   Low-rank solution to an optimization problem constrained by the Navier–Stokes equations. SIAM J. Sci. Comput., 39(1):A255–A280, 2017. (With M. Stoll)

[27]   Low-rank solvers for unsteady Stokes-Brinkman optimal control problem with random data. Computer Methods in Applied Mechanics and Engineering, 304:26–54, 2016. (With P. Benner, A. Onwunta and M. Stoll)

[28]   Fast tensor product solvers for optimization problems with fractional differential equations as constraints. Applied Mathematics and Computation, 273:604 – 623, 2016. (With J. W. Pearson, D. V. Savostyanov and M. Stoll)

[29]   Simultaneous state-time approximation of the chemical master equation using tensor product formats. Numer. Linear Algebra Appl., 22(2):197–219, 2015. (With B. Khoromskij)

[30]   Polynomial Chaos Expansion of random coefficients and the solution of stochastic partial differential equations in the Tensor Train format. SIAM J. Uncertainty Quantification, 3(1):1109–1135, 2015. (With B. N. Khoromskij, A. Litvinenko and H. G. Matthies)

[31]   On evolution of solution times for the chemical master equation of the enzymatic futile cycle. Russian Journal of Numerical Analysis and Mathematical Modelling, 30(1):37–42, 2015. (With E. Tyrtyshnikov)

[32]   Corrected one-site density matrix renormalization group and alternating minimal energy algorithm. In Numerical Mathematics and Advanced Applications — ENUMATH 2013, volume 103, pages 335–343, 2015. (With D. V. Savostyanov)

[33]   Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm., 185(4):1207–1216, 2014. (With B. N. Khoromskij, I. V. Oseledets and D. V. Savostyanov)

[34]   Alternating minimal energy methods for linear systems in higher dimensions. SIAM J. Sci. Comput., 36(5):A2248–A2271, 2014. (With D. V. Savostyanov)

[35]   Low-rank approximation in the numerical modeling of the Farley-Buneman instability in ionospheric plasma. J. Comput. Phys., 263:268–282, 2014. (With A. P. Smirnov and E. E. Tyrtyshnikov)

[36]   Exact NMR simulation of protein-size spin systems using tensor train formalism. Phys. Rev. B, 90:085139, 2014. (With D. V. Savostyanov, J. M. Werner and I. Kuprov)

[37]   Two-level QTT-Tucker format for optimized tensor calculus. SIAM J. on Matrix An. Appl., 34(2):593–623, 2013. (With B. Khoromskij)

[38]   TT-GMRES: solution to a linear system in the structured tensor format. Russ. J. Numer. Anal. Math. Model., 28(2):149–172, 2013.

[39]   Fast solution of multi-dimensional parabolic problems in the tensor train/quantized tensor train–format with initial application to the Fokker-Planck equation. SIAM J. Sci. Comput., 34(6):A3016–A3038, 2012. (With B. N. Khoromskij and I. V. Oseledets)

[40]   Superfast Fourier transform using QTT approximation. J. Fourier Anal. Appl., 18(5):915–953, 2012. (With B. N. Khoromskij and D. V. Savostyanov)

[41]   Low-rank tensor structure of solutions to elliptic problems with jumping coefficients. J. Comput. Math., 30(1):14–23, 2012. (With B. N. Khoromskij, I. V. Oseledets and E. E. Tyrtyshnikov)

[42]   A reciprocal preconditioner for structured matrices arising from elliptic problems with jumping coefficients. Linear Algebra Appl., 436(9):2980–3007, 2012. (With B. N. Khoromskij, I. V. Oseledets and E. E. Tyrtyshnikov)

[43]   Solution of linear systems and matrix inversion in the TT-format. SIAM J. Sci. Comput., 34(5):A2718–A2739, 2012. (With I. V. Oseledets)