arXiv subjects

Suman Chakraborty

Publications and source records attributed to Suman Chakraborty.

At least 19 recordsLinked to original sources

arXiv2026

Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures

Accurate prediction of vapor--liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of composition and hydrocarbon chain length. While deep learning models can provide accurate predictions, they often lack interpretability and explicit analytical expressions. In this work, we propose a symbolic machine learning approach to discover interpretable symbolic corrections to Peng-Robinson equation-of-state (PR-EOS) predictions from experimental data. The proposed approach adopts a two-level strategy: symbolic expressions are first identified for individual hydrocarbon systems, after which their coefficients are represented as functions of carbon number to enable accurate prediction across different hydrocarbon systems. The results demonstrate significantly improved prediction accuracy over the original PR-EOS across all hydrocarbon-nitrogen systems. Overall, the proposed approach provides an interpretable symbolic correction framework for improving PR-EOS predictions of hydrocarbon-nitrogen VLE.

cs.AI↗

arXiv2026

Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces

Spreading of liquid droplets on solid substrates constitutes a classic multiphysics problem with widespread applications ranging from inkjet printing, spray cooling, to biomedical microfluidic systems. Yet, accurate computational fluid dynamic (CFD) simulations are prohibitively expensive, taking more than 18 to 24 hours for each transient computation. In this paper, Physics-Informed Laplace Operator Neural Network (PI-LNO) is introduced, representing a novel architecture where the Laplace integral transform function serves as a learned physics-informed functional basis. Extensive comparative benchmark studies were performed against five other state-of-the-art approaches: UNet, UNet with attention modules (UNet-AM), DeepONet, Physics-Informed UNet (PI-UNet), and Laplace Neural Operator (LNO). Through complex Laplace transforms, PI-LNO natively models the exponential transient dynamics of the spreading process. A TensorFlow-based PI-LNO is trained on multi-surface CFD data spanning contact angles $θ_s ε[20,160]$, employing a physics-regularized composite loss combining data fidelity (MSE, MAE, RMSE) with Navier-Stokes, Cahn-Hilliard, and causality constraints.

cs.LG↗

arXiv2026

Lattice-Boltzmann-Driven Physics-Informed Neural Networks for Droplet Wettability on Rough Surfaces

We introduce a Lattice-Boltzmann-driven kinetic physics-informed neural network (K-PINN) for predictive modeling of droplet dynamics on structured surfaces, in which the discrete Boltzmann-BGK equation is incorporated into the learning framework. Different from traditional PINNs that are restricted by macroscopic continuum equations, the K-PINN framework is built on the mesoscopic kinetic level, in which the essential Lattice-Boltzmann physics is preserved in the data-efficient neural network. The K-PINN has been successfully employed for modeling non-trivial droplet phenomena such as contact pinning, anisotropic spreading, and capillary hysteresis on substrates of different morphologies, ranging from random roughness to periodic pillar structures. Moreover, strict physical consistency, such as mass conservation within 1.5%, is ensured in the K-PINN framework. Furthermore, the U-Net-based encoder-decoder structure of the K-PINN results in a 50-75% reduction in error compared to traditional neural networks, achieving almost perfect agreement with high-resolution Lattice-Boltzmann simulations $L_2$ ~ 0.021-0.026, $R^2$ ~ 0.999. Robust convergence of the K-PINN to diverse surface morphologies is ensured through curriculum learning and adaptive two-phase optimization. Upon convergence, the K-PINN can perform real-time prediction with over $10^4$ evaluations per second. Through the combination of kinetic theory and physics-informed learning, this work establishes a new paradigm for fast, physically consistent modeling of multiphase flows on complex surfaces.

cs.CE↗

arXiv2026

$μ$-FlowNet: A Deep Learning Approach for Mapping Flow Fields in Irregular Microchannels Using an Attention-based U-Net Encoder-Decoder Architecture

In the complex domain of microfluidics systems, analysing fluid flow patterns through random-shaped circular microchannels is significantly challenging task. Conventional approach of solving such problems using computational fluid dynamics often incapable due to their intensive computational requirements and high simulation times. In this study, addressing these limitations, we introduce $μ$-FlowNet, a deep learning framework based on the adaptable U-Net autoencoders. This model provides a data-driven approach that enhances the prediction and mapping of random-shaped circular microchannels and their corresponding fluid flow patterns. The datasets required for the training of the model is generated by performing extensive simulations using conventional approach of computational fluid dynamics methods. The datasets are then pre-processed and accessed the required spatial and temporal features that are essential for the training. We have trained three different models based on U-Net framework namely, standard U-Net, T-Net, and U-Net with attention mechanism to compare the prediction accuracy and loss. The accuracy of the $μ$-FlowNet is compared using metrics of dice score and intersection over union and it shows that U-Net with attention mechanism shows the highest dice score and IoU of 0.9317 and 0.8731, respectively and shows the highest structural similarity as compared to standard U-Net and T-Net. This show that U-Net with attention mechanism serves best model to map the fluid flow pattern with random datasets on testing.

cs.CE↗

arXiv2026

Amalgamation of Physics-Informed Neural Network and LBM for the Prediction of Unsteady Fluid Flows in Fractal-Rough Microchannels

One of the biggest challenges in the optimization of micro-scale fluid transport phenomena is the prediction of unsteady fluid flow in the presence of rough channel walls. Even though the accuracy of available computational fluid dynamics (CFD) solvers such as the lattice Boltzmann method (LBM) is satisfactory, the computational cost of design exploration is very high due to the diverse range of geometries and flow regimes involved in microchannel flows. The present paper introduces a revolutionary concept of a ground-breaking physics-informed neural network (PINN) that utilizes sparse lattice Boltzmann data in combination with the Navier-Stokes equations for the prediction of unsteady fluid flow in fractal-rough microchannels. The roughness of the channel walls is represented by the Weierstrass-Mandelbrot function, considering the characteristics of the surface roughness in real-life problems. The constraints of the Navier-Stokes equations are incorporated in the loss function of the PINN concept for achieving accuracy at much lower computational costs of 150-200 times fewer data points. The validation of the accuracy of the reconstruction of the flow fields is carried out for different Reynolds numbers ranging from Re = 1 to 45 and different amplitude values of the rough channel walls ranging from 5 to 20 lattice units.

cs.CE↗

arXiv2026

Extending deep learning U-Net architecture for predicting unsteady fluid flows in textured microchannels

In this study, we have explored an application of deep learning architecture of the U-Net model, originally designed for biomedical image segmentation, in a regression analysis aimed at predicting fluid flows through textured microchannels. The data for this analysis is generated using the lattice Boltzmann method through extensive simulations, capturing the intricate behaviors of fluid dynamics in a microscale environment. The raw simulation data was meticulously preprocessed to prepare it for training the U-Net model, ensuring that the input features and labels were appropriately formatted and normalized to optimize the learning process of the model. The U-Net model, with its inherent capability of capturing spatial hierarchies and producing better predictions, proved effective in this novel application. We have evaluated the performance of the model using metrics including MSE, RMSE, MAE, and $R^2$ scores. These metrics were crucial in assessing the accuracy and reliability of the model predictions. The results demonstrate that the U-Net model can predict fluid flows with high accuracy and less error, indicating its potential for broader applications in fluid dynamics and other fields requiring precise regression modeling. A parametric analysis of the U-Net with attention mechanism showed that the velocity field prediction is contingent upon the solid-fluid interaction parameter and surface wettability. The U-Net equipped with an attention mechanism predicts the velocity magnitude and components for textured microchannels with an average error of 5.18%, which upon optimization may subsequently lower to 2.1%. The U-Net model including an attention mechanism (U-Net AM) regularly surpasses the conventional U-Net model in all measures, evidencing enhanced accuracy and generalization.

cs.CE↗

arXiv2026

Dynamic Wettability Modulation of Textured, Soft and LIS Interfaces Using Electrowetting

Electrowetting on textured and lubricant infused surfaces is conventionally expected to promote enhanced droplet spreading by reducing apparent contact angles. Contrary to this intuition, we report rapid tangential droplet ejection at applied DC voltages on specific microtextured, lubricant infused surfaces. Using high speed imaging and a precisely controlled electrowetting setup, we reveal the dependence of droplet dynamics on surface topology, wetting state, and the presence of a lubricant. On densely textured thick PDMS substrates of post spacing 5 to 10 um in a low hysteresis non-wetting Cassie state, and on all lubricant infused textured surfaces, droplets experience sudden lateral motion and eventual detachment. We attribute this counterintuitive phenomenon to unbalanced electrocapillary forces at the contact line combined with minimal pinning, which allows asymmetries in electric stresses to translate directly into net lateral motion. In contrast, Wenzel state droplets or surfaces with larger texture spacing exhibit conventional spreading with strong adhesion. By capturing the fundamental interplay among electrostatic driving forces, contact line pinning, and interfacial mobility, our results provide a new paradigm for controlled droplet transport and ejection in electrowetting systems mediated by dense micro posts and lubricant induced interfaces.

cond-mat.soft↗

arXiv2026

Sparse random graphs with many triangles

In this paper we consider the Erdős-Rényi random graph in the sparse regime in the limit as the number of vertices $n$ tends to infinity. We are interested in what this graph looks like when it contains many triangles, in two settings. First, we derive asymptotically sharp bounds on the probability that the graph contains a large number of triangles. We show that conditionally on this event, with high probability the graph contains an almost complete subgraph, i.e., the triangles form a near-clique, and has the same local limit as the original Erdős-Rényi random graph. Second, we derive asymptotically sharp bounds on the probability that the graph contains a large number of vertices that are part of a triangle. If order $n$ vertices are in triangles, then the local limit (provided it exists) is different from that of the Erdős-Rényi random graph. Our results shed light on the challenges that arise in the description of real-world networks, which often are sparse, yet highly clustered, and on exponential random graphs, which often are used to model such networks.

math.PR↗

arXiv2026

Adaptive dynamics of eco-evolutionary repeated games: Effect of reward and punishment

Long-term evolutionary processes can strongly influence common-pool resource conservation by generating new traits or behaviours that modify the feedback between population strategies and the resource state. Here we develop an eco-evolutionary framework in which individuals repeatedly interact with the same opponent and follow direct reciprocity through reactive strategies. The strategic dynamics is coupled to a renewable common resource and analyzed using adaptive dynamics. After our exhaustive non-linear dynamical analysis of $2\times2$ strategic games, we focus on comparative and combined usefulness of institutional incentives in the form of rewards and punishments in preventing the Tragedy of the Commons even when defection dominates in the replete resource state. We also report possibility of robust stable oscillations -- emerging via Hopf bifurcation -- in resource state and population strategies.

q-bio.PE↗

arXiv2026

Oscillatory evolutionarily stable state and limit cycle in replicator dynamics

The idea of evolutionarily stable state (ESS) of a population is a cornerstone of evolutionary game theory; moreover, it coincides with the game-theoretic concept of Nash equilibrium. Such a state corresponds to a strategy adopted by the population such that a rare mutant strategy cannot invade the population. In parallel, the dynamical formulation of evolutionary game theory -- particularly through replicator dynamics embodying the tenet of survival of the fittest -- provides a framework for modelling frequency-dependent selection over time. While it is well known that an ESS corresponds to stable fixed point in replicator dynamics, the evolutionary game-theoretic characterization of limit cycles is unknown. Here we fill this lacuna by defining oscillatory ESS (OESS) which we prove to be a stable limit cycle. We also show when an OESS is unique and if there are multiple OESSes, then what their locations are in the phase space.

nlin.AO↗

arXiv2025

Molecular Dynamics Investigation of Mass Transport During Evaporation for the Binary System of n-Dodecane and Nitrogen

The study of interfacial fluxes under evaporative or condensation processes are ubiquitous in thermal systems, propulsion devices, and many other engineering applications. Most continuum scale models fail to capture the true nature of thermodynamic property variation across the interface, particularly under high-temperature and high-pressure conditions. An improvement over the sharp interface assumption of such continuum scale models is the consideration of a diffused interface and using Kinetic Boundary Conditions (KBCs) to model the mass-transport across the liquid vapor interface. Prior studies on KBCs mainly address monoatomic fluids. Two of the main ingredients required to form KBCs are: density and mass flux. Here, we study a Type-III binary mixture of n-dodecane and nitrogen using non-equilibrium molecular dynamics at near-critical temperatures. Interfacial properties such as thickness, density gradient, and surface tension were analyzed. A key result is the temporal evolution of the evaporation and reflected mass fluxes across the vapor-liquid interface. We observe that both the evaporation and reflection fluxes increase with increasing temperature, indicating enhanced molecular activity and mass transport across the interface at higher Tr. In contrast, the evaporation coefficient alpha_evap decreases from about alpha approximately 0.978 at Tr equals 0.70 to alpha approximately 0.905 at Tr equals 0.95 because the reflected-out flux increases along with the evaporation flux, which reduces the net efficiency of molecular evaporation across the interface. To the authors' knowledge, this is one of the very few studies estimating mass transport coefficients for Type-III binary systems, laying the foundation for KBCs in hydrocarbon and nitrogen mixtures.

physics.comp-ph↗

arXiv2025

Evolutionarily stable strategy in asymmetric games: Dynamical and information-theoretical perspectives

Evolutionarily stable strategy (ESS) is the defining concept of evolutionary game theory. It has a fairly unanimously accepted definition for the case of symmetric games which are played in a homogeneous population where all individuals are in same role. However, in asymmetric games, which are played in a population with multiple subpopulations (each of which has individuals in one particular role), situation is not as clear. Various generalizations of ESS defined for such cases differ in how they correspond to fixed points of replicator equation which models evolutionary dynamics of frequencies of strategies in the population. Moreover, some of the definitions may even be equivalent, and hence, redundant in the scheme of things. Along with reporting some new results, this paper is partly indented as a contextual mini-review of some of the most important definitions of ESS in asymmetric games. We present the definitions coherently and scrutinize them closely while establishing equivalences -- some of them hitherto unreported -- between them wherever possible. Since it is desirable that a definition of ESS should correspond to asymptotically stable fixed points of replicator dynamics, we bring forward the connections between various definitions and their dynamical stabilities. Furthermore, we find the use of principle of relative entropy to gain information-theoretic insights into the concept of ESS in asymmetric games, thereby establishing a three-fold connection between game theory, dynamical system theory, and information theory in this context. We discuss our conclusions also in the backdrop of asymmetric hypermatrix games where more than two individuals interact simultaneously in the course of getting payoffs.

q-bio.PE↗

arXiv2025

Dynamical Response of Deformable Microchannels under Pressure-Driven Flow of Aqueous Polymer Solutions

Microfluidic channels are integral to biomedical technology and process engineering, offering versatility in handling fluids with complex properties, often a combination of viscous and elastic attributes. Despite significant advancements in understanding small-scale fluid-structure interactions, however, experimental insights on the flow of complex fluids in deformable microchannels remain limited. Here, we present controlled experiments using polymer solutions as model viscoelastic fluids to examine the effects of polymer concentration on the elasto-mechanical characteristics of slender cylindrical microchannels. The findings indicate significant differences in fluid-structure interactions between dilute and semi-dilute polymer solutions with varying molecular weights. At higher polymer concentrations, these interactions intensify, leading to reduced pressure drops in high-shear regions and increased pressure drops in low-shear areas, linked to local wall deformation. The increased elasticity of higher concentration solutions further enhances local deformation, disrupts flow, and dissipates energy, resulting in a non-linear rise in pressure drop. This behaviour is aggravated by the solutions increased apparent viscosity due to the entangled polymer network. A theoretical model of flow-induced deformation is also developed, accounting for polymer chain extensibility. These insights highlight the importance of polymer constitution in optimizing the flow characteristics, advancing the development of adaptive microfluidic devices in biological and industrial applications for optimal performance.

cond-mat.soft↗

arXiv2025

When Blood Parts Ways: Phase Separation in Microstructured Environments

Understanding how red blood cell (RBC) suspensions navigate porous materials is critical for for both fundamental physiology, such as maternal-fetal exchange in the placenta, and transformative biomedical applications, including rapid, low-cost disease diagnostics from a single drop of blood in resource-constrained settings. Here we elucidate how RBC movement through fibrous microporous structures is influenced by cell aggregation agents, emphasizing the impact of their clustering, membrane flexibility, and confinement. By varying the volume fraction of the RBC (hematocrit) and aggregation strength, we reveal a surprising phase separation: a dense RBC core surrounded by a cell-free layer, an effect not previously reported in whole blood studies. This separation is shown to be more pronounced with rigidified cells and persists even at high hematocrit levels, unlike in healthy samples. By connecting RBC deformability and aggregability to pore-mediated phase dynamics, our study provides a foundation for new diagnostic tools capable of classifying blood disorders or evaluating blood quality using only a sheet of structured paper, seamlessly integrating fundamental fluid mechanics with translational biomedical innovation in a previously unexplored manner.

cond-mat.soft↗

arXiv2025

Tame sparse exponential random graphs

In this paper, we obtain a precise estimate of the probability that the sparse binomial random graph contains a large number of vertices in a triangle. The estimate of log of this probability is correct up to second order, and enables us to propose an exponential random graph model based on the number of vertices in a triangle. Specifically, by tuning a single parameter, we can with high probability induce any given fraction of vertices in a triangle. Moreover, in the proposed exponential random graph model we derive the large deviation principle for the number of edges. As a byproduct, we propose a consistent estimator of the tuning parameter.

math.PR↗

arXiv2025

Dynamics of two Interacting Drops in a Microfluidic Confinement under imposed Temperature Gradient

Thermocapillary motion is widespread in both natural and engineering applications. A tiny drop of one liquid, suspended within another, may be set into motion aligned with an imposed thermal gradient, as influenced by thermocapillary action stemming from the gradients in interfacial tension due to the local variations in temperature. In real-world situations, however, such drops do not remain in isolation, as they interact with their neighboring entities, including other drops in proximity as well as a nearby solid boundary, setting up a complex interplay between the confinement-mediated interactions and the three-dimensional nature of the droplet dynamics. In this study, we present numerical solutions for the migration dynamics of a tightly confined drop couple, incorporating deformable interfaces, film flow, and Marangoni effects in the presence of dynamically evolving thermocapillary stresses induced by an imposed uniform temperature gradient. Unlike prior investigations, our work highlights the influence of the confinement towards orchestrating non-trivial features of drop migration, as dictated by an intricate coupling of the thermal and flow fields amidst the interferences of the domain boundaries. The study reveals that hydrodynamic interactions resulting from a juxtaposition of these influences deform the drops in a unique manner as compared to the characteristics evidenced by previously reported studies, causing a distortion of the local thermal fields around them. This, in turn, leads to changes in the local thermocapillary stress, affecting the local shear gradient in a manner that alters the local flow field in accordance with ensuring the interfacial stress balance.

physics.flu-dyn↗

arXiv2025

Nano-size fragmentation of Tantalum in Copper composite using additive manufacturing

The biggest challenge in manufacturing an immiscible system is phase segregation and non-uniformity inside the composite matrix. Additive manufacturing has the potential to overcome these difficulties due to the high cooling rate achieved during the process. Here we have developed immiscible Copper-based composites reinforced with Tantalum, which were fabricated using the powder bed fusion melting (PBF-M) technique. The distinct advantage of utilizing Tantalum in this process resides in its high melting point, allowing it to remain in particle form within the composite and contribute to its mechanical and surface/wear properties. The PBF-M results in the in situ fragmentation of micron-size Tantalum particles into nanoparticle form through a surface roughening process during laser interaction, enhancing its mechanical and wear properties. The microstructural evolution of Cu-Ta composites is explained through multiscale numerical modeling. The enhanced yield strength and the dynamics of the Ta particles were corroborated by molecular dynamics simulations. The maximum yield strength is exhibited by Cu-5wt%Ta of 80 MPa. Addition of Ta also have significant improvement in wear properties of composites. The current results can be exploited to develop complex shape, high energy efficient copper-based composites.

cond-mat.mtrl-sci↗

arXiv2024

Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters

Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.

cs.LG↗