Sergey Dolgov | DeepAI

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∙ 05/18/2023

Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method

The solution of computational fluid dynamics problems is one of the most...

0 Egor Kornev, et al. ∙

∙ 03/05/2023

Self-reinforced polynomial approximation methods for concentrated probability densities

Transport map methods offer a powerful statistical learning tool that ca...

0 Tiangang Cui, et al. ∙

∙ 02/16/2023

A weighted subspace exponential kernel for support tensor machines

High-dimensional data in the form of tensors are challenging for kernel ...

0 Kirandeep Kour, et al. ∙

∙ 01/20/2023

State-constrained Optimization Problems under Uncertainty: A Tensor Train Approach

We propose an algorithm to solve optimization problems constrained by pa...

0 Harbir Antil, et al. ∙

∙ 09/05/2022

Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation

We propose a deep importance sampling method that is suitable for estima...

0 Tiangang Cui, et al. ∙

∙ 08/15/2022

Undersampling Raster Scans in Spectromicroscopy for reduced dose and faster measurements

Combinations of spectroscopic analysis and microscopic techniques are us...

0 Oliver Townsend, et al. ∙

∙ 11/09/2021

TTRISK: Tensor Train Decomposition Algorithm for Risk Averse Optimization

This article develops a new algorithm named TTRISK to solve high-dimensi...

0 Harbir Antil, et al. ∙

∙ 06/08/2021

Conditional Deep Inverse Rosenblatt Transports

We present a novel offline-online method to mitigate the computational b...

0 Tiangang Cui, et al. ∙

∙ 07/31/2020

Functional Tucker approximation using Chebyshev interpolation

This work is concerned with approximating a trivariate function defined ...

0 Sergey Dolgov, et al. ∙

∙ 07/14/2020

Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports

Characterising intractable high-dimensional random variables is one of t...

0 Tiangang Cui, et al. ∙

∙ 02/12/2020

Efficient Structure-preserving Support Tensor Train Machine

Deploying the multi-relational tensor structure of a high dimensional fe...

0 Kirandeep Kour, et al. ∙

∙ 01/22/2020

Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format

Low rank tensor approximations have been employed successfully, for exam...

0 Paul B. Rohrbach, et al. ∙

∙ 12/14/2019

Solving differential Riccati equations: A nonlinear space-time method using tensor trains

Differential algebraic Riccati equations are at the heart of many applic...

0 Tobias Breiten, et al. ∙

∙ 08/05/2019

A Tensor Decomposition Approach for High-Dimensional Hamilton-Jacobi-Bellman Equations

A tensor decomposition approach for the solution of high-dimensional, fu...

0 Sergey Dolgov, et al. ∙

∙ 04/21/2019

Kriging in Tensor Train data format

Combination of low-tensor rank techniques and the Fast Fourier transform...

0 Sergey Dolgov, et al. ∙

∙ 10/02/2018

Approximation and sampling of multivariate probability distributions in the tensor train decomposition

General multivariate distributions are notoriously expensive to sample f...

0 Sergey Dolgov, et al. ∙

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