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Dmitry Grishchenko

Publications and source records attributed to Dmitry Grishchenko.

8 recordsLinked to original sources
arXiv2025

Data-driven optimization of sparse sensor placement in thermal hydraulic experiments

Thermal-Hydraulic (TH) experiments provide valuable insight into the physics of heat and mass transfer and qualified data for code development, calibration and validation. However, measurements are typically collected from sparsely distributed sensors, offering limited coverage over the domain of interest and phenomena of interest. Determination of the spatial configuration of these sensors is crucial and challenging during the pre-test design stage. This paper develops a data-driven framework for optimizing sensor placement in TH experiments, including (i) a sensitivity analysis to construct datasets, (ii) Proper Orthogonal Decomposition (POD) for dimensionality reduction, and (iii) QR factorization with column pivoting to determine optimal sensor configuration under spatial constraints. The framework is demonstrated on a test conducted in the TALL-3D Lead-bismuth eutectic (LBE) loop. In this case, the utilization of optical techniques, such as Particle Image Velocimetry (PIV), are impractical. Thereby the quantification of momentum and energy transport relies heavily on readings from Thermocouples (TCs). The test section was previously instrumented with many TCs determined through a manual process combining simulation results with expert judgement. The proposed framework provides a systematic and automated approach for sensor placement. The resulting TCs exhibit high sensitivity to the variation of uncertain input parameters and enable accurate full field reconstruction while maintaining robustness against measurement noise.

eess.SY↗
arXiv2025

Data-driven modeling for flow reconstruction from sparse temperature measurements

Measurement of the velocity field in thermal-hydraulic experiments is of great importance for phenomena interpretation and code validation. Direct measurement employing Particle Image Velocimetry (PIV) is challenging in some multiphase scenarios where the measurement system would be strongly affected by the phase interaction. In such cases, measurement can only be achieved via sparsely distributed sensors, such as Thermocouples (TCs) and pressure transducers. An example can refer to steam injection into a water pool where the rapid collapse of bubbles and significant temperature gradient make it impossible to obtain the main flow velocity at a large steam flux by PIV. This work investigates the feasibility and capability of utilization of data-driven modeling for flow reconstruction from sparse temperature data. The framework applies (i) a Proper Orthogonal Decomposition (POD) to encode variables from full space to latent space and (ii) a Fully connected Neural Network (FNN) to approximate sparse measurements to coefficients of latent space. Sensor positioning aiming to identify the optimal sensor location is also discussed. The proposed framework has been tested on a single-phase planar jet and steam condensing jets issued through a multi-hole sparger.

physics.flu-dyn↗
arXiv2021

Adaptive Catalyst for Smooth Convex Optimization

In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization problems. The main approach of our envelope is the same as in Catalyst (Lin et al., 2015): an accelerated proximal outer gradient method, which is used as an envelope for a non-accelerated inner method for the $\ell_2$ regularized auxiliary problem. Our algorithm has two key differences: 1) easily verifiable stopping criteria for inner algorithm; 2) the regularization parameter can be tunned along the way. As a result, the main contribution of our work is a new framework that applies to adaptive inner algorithms: Steepest Descent, Adaptive Coordinate Descent, Alternating Minimization. Moreover, in the non-adaptive case, our approach allows obtaining Catalyst without a logarithmic factor, which appears in the standard Catalyst (Lin et al., 2015, 2018).

math.OC↗
arXiv2020

Randomized Progressive Hedging methods for Multi-stage Stochastic Programming

Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario subproblems have to be solved at each iteration. In this paper, we introduce randomized versions of the Progressive Hedging algorithm able to produce new iterates as soon as a single scenario subproblem is solved. Building on the relation between Progressive Hedging and monotone operators, we leverage recent results on randomized fixed point methods to derive and analyze the proposed methods. Finally, we release the corresponding code as an easy-to-use Julia toolbox and report computational experiments showing the practical interest of randomized algorithms, notably in a parallel context. Throughout the paper, we pay a special attention to presentation, stressing main ideas, avoiding extra-technicalities, in order to make the randomized methods accessible to a broad audience in the Operations Research community.

cs.DC↗
arXiv2020

Distributed Learning with Sparse Communications by Identification

In distributed optimization for large-scale learning, a major performance limitation comes from the communications between the different entities. When computations are performed by workers on local data while a coordinator machine coordinates their updates to minimize a global loss, we present an asynchronous optimization algorithm that efficiently reduces the communications between the coordinator and workers. This reduction comes from a random sparsification of the local updates. We show that this algorithm converges linearly in the strongly convex case and also identifies optimal strongly sparse solutions. We further exploit this identification to propose an automatic dimension reduction, aptly sparsifying all exchanges between coordinator and workers.

math.OC↗
arXiv2020

Proximal Gradient methods with Adaptive Subspace Sampling

Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose a randomized proximal gradient method harnessing this underlying structure. We introduce two key components: i) a random subspace proximal gradient algorithm; ii) an identification-based sampling of the subspaces. Their interplay brings a significant performance improvement on typical learning problems in terms of dimensions explored.

math.OC↗
arXiv2017

Privacy Preserving Randomized Gossip Algorithms

In this work we present three different randomized gossip algorithms for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes. We give iteration complexity bounds for all methods, and perform extensive numerical experiments.

math.OC↗