arXiv subjects

Aleksey Khlyupin

Publications and source records attributed to Aleksey Khlyupin.

At least 19 recordsLinked to original sources

arXiv2026

A New Framework for Interfacial Statistics: Exact n-Point Correlations of Gaussian Level Sets

We derive exact analytical expressions for higher-order correlations of Gaussian level-set interfaces, establishing a direct link between bulk field statistics and interface geometry. This framework enables efficient reconstruction of disordered media, detailed modeling of {Gaussian foams}---structured, double-thresholded interfaces with tunable morphology---and analysis of {memory-enhanced anisotropy} reflecting directional persistence in surface structure. These results open new possibilities for characterizing complex transport, guiding stochastic reconstructions, designing materials with desired properties, quantifying the information content of correlation functions and modelling directional processes on irregular boundaries.

cond-mat.stat-mech↗

arXiv2026

Physically Consistent 2D to 3D Pore Space Synthesis: 1. Dynamic Grain Packing Under Strict Morphology Constraints

The 3D digital reconstruction of multi-scale, heterogeneous porous media is traditionally limited by the resolution boundaries and artifacts of X-ray computed tomography (XCT). This paper introduces a physically consistent 2D to 3D pore space synthesis framework that supersedes conventional geometric shuffling with dynamic Newtonian gravitational deposition using a high-performance rigid-body mechanics engine. To honor strict physical contact mechanics, statistical 2D targets, and morphological constraints, we develop a two-stage dynamic packing pipeline that combines realistic 3D grain shapes with a virtual sub-particle insertion engine. The framework is validated against synthetic and natural core benchmarks. First, we show that our stereological integral equation systematically converts size distributions; bypassing this inversion step induces a systematic 50\% deviation in permeability predictions despite identical total porosities, independently altering flow channel topology. Second, temporary virtual spheres serve as architectural placeholders to stabilize large non-equilibrium dissolution voids, ensuring the reconstructed model satisfies target flow metrics within the prescribed tolerance. Finally, when applied to a fine-grained, weakly consolidated sandstone, the workflow exposes and suppresses systematic XCT artifacts, including phantom internal porosity. Ensembles of two-point correlation functions demonstrate that the hydrodynamically and structurally synthesized digital twins quantitatively exhibit closer structural proximity to the uncorrupted rock geometry than the reference XCT dataset itself. This methodology establishes a scalable foundation for high-fidelity multi-phase rock reconstruction, with potential extensions toward complex amorphous cement phases.

physics.comp-ph↗

arXiv2026

Structure-Informed Bayesian Inference of Anomalous Transport and Hidden Molecular Trapping in Amorphous Media

Molecular diffusion in fluctuating amorphous and macromolecular media governs key transport processes across soft-matter physics, energy storage, and biological membranes. Extracting localized trapping states from single-particle tracking trajectories remains a fundamental challenge; because thermal structural breathing continuously reconfigures pore boundaries, conventional geometric algorithms suffer from severe systematic biases, erroneously merging distinct localized states during cyclic molecular returns. Here, we address this deadlock by shifting the paradigm from local geometric recurrence to a structure-informed Bayesian regularization. Leveraging discrete Morse theory, we extract the time-invariant topological skeleton of the fluctuating host matrix to construct robust, gas-specific physical priors that account for individual molecular dimensions. Trajectory steps are sequentially partitioned via a two-stage probabilistic refinement that dynamically adapts to the transport landscape. Benchmarked against a rigorous environment where synthetic particles explore the actual interconnected matrix graph, our approach eliminates systemic biases, restricting macroscopic trapping parameter deviations to just a few percent under optimal linear $O(N)$ computational scaling. Applied to hydrogen and methane transport within a type-I kerogen matrix, serving as a prototype for highly tortuous, flexible macromolecular networks, the method successfully decodes the hidden microscopic mechanisms of confined diffusion. To ensure immediate broad impact, the documented open-source code and data are made publicly available, offering an accessible strategy readily adaptable to a broad spectrum of tracking phenomena, from ion transport in battery polymers to protein trafficking within cellular environments.

physics.comp-ph↗

arXiv2026

Bridging scales in porous media: cDFT-informed pore network modelling for fluid transport with nanoconfined phase behavior

The simulation of fluid flow in real, multiscale porous media remains challenging due to the complexity of nanoscale phenomena and the difficulty of developing upscaling methodologies. In this study, we introduce a multiscale filtration framework based on quasi-static Pore Network Modelling, incorporating the effects of pore blockage resulting from capillary condensation of fluid in the nanoporous space. To accurately predict capillary condensation in nanoconfinement, we apply classical Density Functional Theory calculations considering capillary hysteresis. The pores blocked by condensate are excluded from the fluid flow, resulting in a decrease in permeability of the porous space. Our findings demonstrate that the resulting permeability is strongly dependent on the geometry of the porous space, including pore size distribution, throat size distribution, sample size, and the particular structure of the sample, as well as thermodynamic conditions and processes, specifically pressure growth or decrease. Overall, the presented research contributes valuable insights into multiscale transport phenomena and facilitates the advancement of upscaling techniques.

physics.flu-dyn↗

arXiv2024

The role of surface material properties on the behavior of ionic liquids in nanoconfinement: A critical review and perspective

Room temperature ionic liquids show great promise as electrolytes in various technological applications, such as energy storage or electrotunable lubrication. These applications are particularly intriguing due to the specific behavior of ionic liquids in nanoconfinement. While previous research has been focused on optimizing the required characteristics through the selection of electrolyte properties, the contribution of confining material properties in these systems has been largely overlooked. In this Review, we provide constructive analysis of recent developments related to the description of surface material properties impact on the ionic liquid behavior in the confinement and propose potential ways for further investigations in this direction. Although the presented advances reveal the importance of surface material properties in the application processes with confined ionic liquids, there are still a lot of issues that should be thoroughly investigated in future. We believe that this review will significantly contribute to the development of new approaches with material properties consideration for confined ionic liquid research.

cond-mat.soft↗

arXiv2024

Mechanism of overscreening breakdown by electrode surface morphology in asymmetric ionic liquids

The interfacial nature of the electric double layer (EDL) assumes that electrode surface morphology significantly impacts the EDL properties. Since molecular-scale roughness modifies the structure of EDL, it is expected to disturb the overscreening effect and alter differential capacitance (DC). In this Letter, we present a model that describes EDL near atomically rough electrodes with account for short-range electrostatic correlations. We provide numerical and analytical solutions for the analysis of conditions for the overscreening breakdown and DC shift estimation. Our findings reveal that electrode surface structure leads to DC decrease and can both brake or enhance overscreening depending on the relation of surface roughness to electrostatic correlation length and ion size asymmetry.

physics.chem-ph↗

arXiv2023

Prior Global Search Stability on Finite Graphs with Uncertainty. May Greedy Search Win?

This research paper addresses the stability of search algorithms in complex networks when dealing with incomplete information or uncertainty. We propose a theoretical model to investigate whether a global search algorithm with incomplete prior information can be outperformed by a stochastic greedy search on average. The model incorporates random variables to perturb edge weights in the graph, thus capturing the uncertainty of available information. Our findings indicate that some graphs and uncertainty model parameters exist where the global search algorithm fails under uncertainty conditions, while the random greedy search performs better. We derive a critical curve that separates stable from unstable graphs for global search with incomplete information. Interestingly, the critical curve's behavior changes from monotonic to bell-shaped depending on the uncertainty parameters. We test our proposed model through numerical simulations on various synthetic and real-world graphs with different structures. Our results offer insights into the design and optimization of search algorithms for network-based applications, such as communication networks, social networks, and biological networks. We also discuss the study of memory and associative learning in miniature insects, highlighting the potential of efficient search and walking strategies for small robots or devices that operate in a limited area in space.

cs.SI↗

arXiv2023

Towards effective information content assessment: analytical derivation of information loss in the reconstruction of random fields with model uncertainty

Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic reconstructions, stationarity and representativity analysis. The most important characteristic of any such descriptor is its information content - or its ability to describe the structure at hand. For example, from crystallography it is well known that experimentally measurable $S_2$ correlation function lacks necessary information content to describe majority of structures. The information content of this function can be assessed using Monte-Carlo methods only for very small 2D images due to computational expenses. Some indirect quantitative approaches for this and other correlation function were also proposed. Yet, to date no methodology to obtain information content for arbitrary 2D or 3D image is available. In this work, we make a step toward developing a general framework to perform such computations analytically. We show, that one can assess the entropy of a perturbed random field and that stochastic perturbation of fields correlation function decreases its information content. In addition to analytical expression, we demonstrate that different regions of correlation function are in different extent informative and sensitive for perturbation. Proposed model bridges the gap between descriptor-based heterogeneous media reconstruction and information theory and opens way for computationally effective way to compute information content of any descriptor as applied to arbitrary structure.

physics.data-an↗

arXiv2023

Molecular scale ion separation driven by surface roughness and ion size asymmetry: new analytical solutions for differential capacitance of EDL

Electrode surface roughness significantly impacts the structure of electric double layer on a molecular scale. We derive analytical solutions for differential capacitance (DC) of electric double layer near rough electrode surface, comparing them with a range of experimental and numerical studies. Two causes of ions separation are considered: ion size asymmetry and electrode surface roughness. The model has three scale parameters determining DC properties: the Debye length, difference of ion penetration depths, and surface roughness parameter. For the first time, DC profile with more than two peaks was obtained analytically due to account for ions reorientation effect. The model predicts DC curve transform from bell to camel and inverse induced by electrode surface roughness. The behavior of DC-potential relation with temperature and ion size ratio diverges depending on the type of ions separation. Additionally, we provide analytical solutions for zero charge potential and demonstrate roughness effect on its value. Based on the results, we give recommendations for properties required to design effective electric double layer capacitors.

physics.chem-ph↗

arXiv2022

Adaptive intermolecular interaction parameters for accurate Mixture Density Functional Theory calculations

The description of fluid mixtures molecular behavior is significant for various industry fields due to the complex composition of fluid found in nature. Statistical mechanics approaches use intermolecular interaction potential to predict fluids behavior on the molecular scale. The paper provides a comparative analysis of mixing rules applications for obtaining intermolecular interaction parameters of mixture components. These parameters are involved in the density functional theory equation of state for mixtures (Mixture DFT EoS) and characterize thermodynamic mixture properties in the bulk. The paper demonstrates that Mixture DFT EoS with proper intermolecular parameters agrees well with experimental mixtures isotherms in bulk for different mixtures. However, predictions of vapor-liquid equilibrium (VLE) experimental data are not successful. Halgren HHG, Waldman - Hagler, and adaptive mixing rules that adjust on the experimental data from the literature are used for the first time to obtain intermolecular interaction parameters for the mixture DFT model. The results obtained provide a base for understanding how to validate the DFT fluid mixture model for calculating thermodynamic properties of fluid mixtures on a micro and macro scale.

physics.chem-ph↗

arXiv2021

Linking Theoretical and Simulation Approaches to Study Fluids in Nanoporous Media: Classical Molecular Dynamics and Density Functional Theory

We propose an approach that links density functional theory (DFT) and molecular dynamics (MD) simulation to study fluid behavior in nanopores in contact with bulk (macropores). It consists of two principal steps. First, the theoretical calculation of fluid composition and density distribution in nanopore under specified thermodynamic conditions using DFT. Second, MD simulation of the confined system with obtained characteristics. Thus, we investigate an open system in a grand canonical ensemble. This method allows us to investigate both structural and dynamic properties of confined fluid at given bulk conditions and do not require computationally expensive simulation of bulk reservoir. In this work, we obtain equilibrium density profiles of pure methane, ethane and carbon dioxide and their binary mixtures in slitlike nanopores with carbon walls. Good agreement of structures obtained by theory and simulation confirms the applicability of the proposed method.

physics.comp-ph↗

arXiv2021

Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks

Quite a few people in the world have to stay under permanent surveillance for health reasons; they include diabetic people or people with some other chronic conditions, the elderly and the disabled.These groups may face heightened risk of having life-threatening falls or of being struck by a syncope. Due to limited availability of resources a substantial part of people at risk can not receive necessary monitoring and thus are exposed to excessive danger. Nowadays, this problem is usually solved via applying Human Activity Recognition (HAR) methods. HAR is a perspective and fast-paced Data Science field, which has a wide range of application areas such as healthcare, sport, security etc. However, the currently techniques of recognition are markedly lacking in accuracy, hence, the present paper suggests a highly accurate method for human activity classification. Wepropose a new workflow to address the HAR problem and evaluate it on the UniMiB SHAR dataset, which consists of the accelerometer signals. The model we suggest is based on continuous wavelet transform (CWT) and convolutional neural networks (CNNs). Wavelet transform localizes signal features both in time and frequency domains and after that a CNN extracts these features and recognizes activity. It is also worth noting that CWT converts 1D accelerometer signal into 2D images and thus enables to obtain better results as 2D networks have a significantly higher predictive capacity. In the course of the work we build a convolutional neural network and vary such model parameters as number of spatial axes, number of layers, number of neurons in each layer, image size, type of mother wavelet, the order of zero moment of mother wavelet etc. Besides, we also apply models with residual blocks which resulted in significantly higher metric values. Finally, we succeed to reach 99.26 % accuracy and it is a worthy performance for this problem.

cs.CV↗

arXiv2021

Variation Free Approach for Molecular Density Functional Theory: Data-driven Stochastic Optimization

Density functional theory (DFT) is an efficient instrument for describing a wide range of nanoscale phenomena: wetting transition, capillary condensation, adsorption, etc. In this paper, we suggest a method for obtaining the equilibrium molecular fluid density in a nanopore using DFT without calculating the free energy variation - Variation Free Density Functional Theory (VF-DFT). This technique can be used to explore confined fluids with a complex type of interactions, additional constraints and to speed up calculations, which might be crucial in an inverse problems. The fluid density in VF-DFT approach is represented as a decomposition over a limited set of basis functions. We applied Principal Component Analysis (PCA) to extract the basic patterns from the density function and take them into account in the construction of a set of basis functions. The decomposition coefficients of the fluid density by the basis were sought by stochastic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO) to minimize the free energy of the system. In this work, two different fluids were studied: nitrogen at a temperature of 77.4 K and argon 87.3 K, at a pore of 3.6 nm, and the performance of optimization algorithms was compared. We also introduce the Hybrid Density Functional Theory (H-DFT) approach based on stochastic optimization methods and the classical Picard iteration method to find the equilibrium fluid density starting from the physically appropriate solution. The combination of Picard iteration and stochastic algorithms helps to significantly speed up the calculations of equilibrium density in the system without losing the quality of the solution, especially in cases with the high relative pressure and expressed layering structure.

physics.comp-ph↗

arXiv2021

Adaptive phase-retrieval stochastic reconstruction with correlation functions: 3D images from 2D cuts

Precise characterization of three-dimensional heterogeneous media is indispensable in finding the relationships between structure and macroscopic physical properties (permeability, conductivity, and others). The most widely used experimental methods (electronic and optical microscopy) provide high-resolution bi-dimensional images of the samples of interest. However, 3D material inner microstructure registration is needed to apply numerous modeling tools. Numerous research areas search for cheap and robust methods to obtain "full" 3D information about the structure of the studied sample from its 2D cuts. In this work, we develop a dynamic phase-retrieval stochastic reconstruction algorithm that can create 3D replicas from 2D original images - DDTF. The DDTF is free of artifacts characteristic of previously proposed phase-retrieval techniques. While based on a two-point $S_2$ correlation function, any correlation function or other morphological metrics can be accounted for during the reconstruction, thus, paving the way to the hybridization of different reconstruction techniques. In this work, we use two-point probability and surface-surface functions for optimization. To test DDTF, we performed reconstructions for three binary porous media samples of different genesis: sandstone, carbonate, and ceramic. Based on computed permeability and connectivity ($C_2$ and $L_2$ correlation functions), we have shown that the proposed technique in terms of accuracy is comparable to the classic simulated annealing-based reconstruction method but is computationally very effective. Our findings open the possibility of utilizing DDTF to produce fast or crude replicas further polished by other reconstruction techniques such as simulated annealing or process-based methods.

cond-mat.mtrl-sci↗

arXiv2020

Branching random graph model of rough surfaces describes thermal properties of the effective molecular potential

Fluid properties near rough surfaces are crucial in describing fundamental surface phenomena and modern industrial material design implementations. One of the most powerful approaches to model real rough materials is based on the surface representation in terms of random geometry. Understanding the influence of random solid geometry on the low-temperature fluid thermodynamics is a cutting edge problem. Therefore this work extends recent studies bypassing high-temperature expansion and small heterogeneity scale. We introduce random branching trees whose topology reflects the hierarchical properties of a random solid geometry. This mathematical representation allows us to obtain averaged free energy using a statistical model of virtual clusters interacting through random ultrametric pairwise potentials. Our results demonstrate that a significant impact to fluid-solid interface energy is induced by the hierarchical structure of random geometry at low temperature. These calculations coincide with direct Monte Carlo simulations. Due to the study's interdisciplinary nature, the developed approach can be applied to a wide range of quenched disorder systems on random graphs.

cond-mat.dis-nn↗

arXiv2020

Objective-Sensitive Principal Component Analysis for High-Dimensional Inverse Problems

We present a novel approach for adaptive, differentiable parameterization of large-scale random fields. If the approach is coupled with any gradient-based optimization algorithm, it can be applied to a variety of optimization problems, including history matching. The developed technique is based on principal component analysis (PCA) but modifies a purely data-driven basis of principal components considering objective function behavior. To define an efficient encoding, Gradient-Sensitive PCA uses an objective function gradient with respect to model parameters. We propose computationally efficient implementations of the technique, and two of them are based on stationary perturbation theory (SPT). Optimality, correctness, and low computational costs of the new encoding approach are tested, verified, and discussed. Three algorithms for optimal parameter decomposition are presented and applied to an objective of 2D synthetic history matching. The results demonstrate improvements in encoding quality regarding objective function minimization and distributional patterns of the desired field. Possible applications and extensions are proposed.

cs.LG↗

arXiv2019

Detailed Characterization of Rough Surfaces for Silica Materials

We propose a new approach to obtain the nanoscale morphology of rough surfaces from low-temperature adsorption experiments. Our method is based on one of the most realistic models of rough surfaces formulated in terms of random correlated processes and random surface density functional theory (RS-DFT) as a theoretical adsorption model. We consider the roughness in the normal direction, the correlation length of the lateral surface structure and the specific surface area as tuning parameters of RS-DFT to fit the experimental data in the low pressure range, where the influence of the surface geometry is the most crucial. One of the major advantages of the proposed approach over published methods is the best-fit detailed geometry of rough surfaces, which provides full information for further atomistic modeling. The obtained geometry correctly reflects how the nanoroughness of silica materials depends on the synthesis conditions. We demonstrate that the surface fractal dimension observed in many experiments is natural for the correlated random surface model. We investigated the surface geometry of popular silica materials synthesized at different conditions. The obtained roughness parameters and fractal dimensions coincide well with the published experimental data. Analysis of the best fit specific surface area reveals the mechanism of adsorption on rough surfaces and provides a new strategy for the search of optimal storage materials.

cond-mat.mtrl-sci↗

arXiv2019

Adaptive Strategies For Efficient Model Reduction In High-Dimensional Inverse Problems

This work explores a novel approach for adaptive, differentiable parametrization of large-scale non-stationary random fields. Coupled with any gradient-based algorithm, the method can be applied to variety of optimization problems, including history matching. The developed technique is based on principal component analysis (PCA), but, in contrast to other PCA-based methods, allows to amend parametrization process regarding objective function behaviour.

math.OC↗