257 citations · 1.1k across the 33 of their papers we have counts for
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Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation
Tiangang Cui, Sergey Dolgov, Robert Scheichl
We propose a deep importance sampling method that is suitable for estimating rare event probabilities in high-dimensional problems. We approximate the optimal importance distributi…
Tensor product approach to modelling epidemics on networks
Sergey V. Dolgov, Dmitry V. Savostyanov
To improve mathematical models of epidemics it is essential to move beyond the traditional assumption of homogeneous well--mixed population and involve more precise information on…
Data-driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations
Sergey Dolgov, Dante Kalise, Luca Saluzzi
A gradient-enhanced functional tensor train cross approximation method for the resolution of the Hamilton-Jacobi-Bellman (HJB) equations associated to optimal feedback control of n…
Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control
Sergey Dolgov, Dante Kalise, Luca Saluzzi
An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the constr…