arXiv subjects

Chaitanya Joshi

Publications and source records attributed to Chaitanya Joshi.

At least 19 recordsLinked to original sources

arXiv2025

Elastocapillary adhesion of soft gel microspheres

Softer means stickier for solid adhesives, because material compliance facilitates close contact between non-conformal surfaces. Recent discoveries have revealed that soft materials can exhibit a rich array of new physics arising from competing effects of continuum elasticity, fluid-like surface mechanics, and internal poroelastic flows, all of which can directly impact interfacial interactions. In this work, we investigate this complex interplay across several orders of magnitude of elastic stiffness by measuring the complete adhesive contact geometry between compliant silicone gel microspheres and flat, rigid substrates. We observe a continuous elastocapillary transition in adhesion mechanics, with novel features revealed by both the breadth of data and the detailed contact geometries. Importantly, soft gel spheres exhibit a remarkably broad range of near-equilibrium contact morphologies and their contact line deformation is always mediated by a fluid contact zone that phase separates from the gel. To explain this, we develop a new model incorporating elastocapillary and poroelastic mechanics that predicts the complete range of adhesive behavior and elucidates energetic tradeoffs. The data and model together reveal a shallow energy landscape that may contribute to the robustness of everyday adhesives.

cond-mat.soft↗

arXiv2025

Utilising Deep Learning to Elicit Expert Uncertainty

Recent work [ 14 ] has introduced a method for prior elicitation that utilizes records of expert decisions to infer a prior distribution. While this method provides a promising approach to eliciting expert uncertainty, it has only been demonstrated using tabular data, which may not entirely represent the information used by experts to make decisions. In this paper, we demonstrate how analysts can adopt a deep learning approach to utilize the method proposed in [14 ] with the actual information experts use. We provide an overview of deep learning models that can effectively model expert decision-making to elicit distributions that capture expert uncertainty and present an example examining the risk of colon cancer to show in detail how these models can be used.

cs.LG↗

arXiv2024

Contrasting the optimal resource allocation to cybersecurity and cyber insurance using prospect theory versus expected utility theory

Protecting against cyber-threats is vital for every organization and can be done by investing in cybersecurity controls and purchasing cyber insurance. However, these are interlinked since insurance premiums could be reduced by investing more in cybersecurity controls. The expected utility theory and the prospect theory are two alternative theories explaining decision-making under risk and uncertainty, which can inform strategies for optimizing resource allocation. While the former is considered a rational approach, research has shown that most people make decisions consistent with the latter, including on insurance uptakes. We compare and contrast these two approaches to provide important insights into how the two approaches could lead to different optimal allocations resulting in differing risk exposure as well as financial costs. We introduce the concept of a risk curve and show that identifying the nature of the risk curve is a key step in deriving the optimal resource allocation.

econ.EM↗

arXiv2024

Spatiotemporal control of structure and dynamics in a polar active fluid

We apply optimal control theory to a model of a polar active fluid (the Toner-Tu model), with the objective of driving the system into particular emergent dynamical behaviors or programming switching between states on demand. We use the effective self-propulsion speed as the control parameter (i.e. the means of external actuation). We identify control protocols that achieve outcomes such as relocating asters to targeted positions, forcing propagating solitary waves to reorient to a particular direction, and switching between stationary asters and propagating fronts. We analyze the solutions to identify generic principles for controlling polar active fluids. Our findings have implications for achieving spatiotemporal control of active polar systems in experiments, particularly in vitro cytoskeletal systems. Additionally, this research paves the way for leveraging optimal control methods to engineer the structure and dynamics of active fluids more broadly.

cond-mat.soft↗

arXiv2023

Eliciting Informative Priors by Modelling Expert Decision Making

This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution for a rare event $A$. More specifically, assuming there exists a decision-making process closely related to $A$ which forms a decision $Y$, where a history of decisions have been collected. By modelling the data observed to make the historic decisions, using a Bayesian model, an analyst can infer a distribution for the parameters of the random variable $Y$. This distribution can be used to approximate the prior distribution for the parameters of the random variable for event $A$. This method is novel in the field of prior elicitation and has the potential of improving upon current methods by using real-life decision-making processes, that can carry real-life consequences, and, because it does not require an expert to have statistical knowledge. Future decision making can be improved upon using this method, as it highlights variables that are impacting the decision making process. An application for eliciting a prior distribution of recidivism, for an individual, is used to explain this method further.

stat.ME↗

arXiv2023

Surfing, sweeping, and assembly of particles by a moving liquid crystal phase boundary

Non-equilibrium transport of particles embedded in a liquid crystal host can, by cooling through a phase transition, be exploited to create a remarkable variety of structures including shells, foams, and gels. Due to the complexity of the multicomponent system and protocol-dependent experimental results, the physical mechanisms behind structure selection remain only partially understood. Here we formulate a new model coupling LC physics to a Fokker-Planck equation as is commonly used in studies of transport. The resulting model allows us to draw an analogy between the LC-nanocomposite system and chemotaxis, enriching the space of possible target structures that could be produced. We study the model in one dimension both analytically and numerically to identify different parameter regimes where soliton-like pulses of particles ``surf'' the phase boundary or where the interface ``sweeps'' particles from one domain to another. We also consider an extended model that includes agglomeration of the particles and observe formation of periodic structures as a prototypical example of hierarchical self assembly. Results are compared with experimental observations of transport by isolated phase boundaries.

cond-mat.soft↗

arXiv2023

From Disks to Channels: Dynamics of Active Nematics Confined to an Annulus

Confinement can be used to systematically tame turbulent dynamics occurring in active fluids. Although periodic channels are the simplest geometries to study confinement numerically, the corresponding experimental realizations require closed racetracks. Here, we computationally study 2D active nematics confined to such a geometry -- an annulus. By systematically varying the annulus inner radius and channel width, we bridge the behaviors observed in the previously studied asymptotic limits of the annulus geometry: a disk and an infinite channel. We identify new steady-state behaviors, which reveal the influence of boundary curvature and its interplay with confinement. We also show that, below a threshold inner radius, the dynamics are insensitive to topological constraints imposed by boundary conditions. We explain this insensitivity through a simple scaling analysis. Our work sheds further light on design principles for using confinement to control the dynamics of active nematics.

cond-mat.soft↗

arXiv2023

Effect of confinement on the mechanics of a swelling hydrogel bead

We recast the problem of hydrogel swelling under physical constraints as an energy optimization problem. We apply this approach to compute equilibrium shapes of hydrogel spheres confined within a jammed matrix of rigid beads, and interpret the results to determine how confinement modifies the mechanics of swollen hydrogels. In contrast to the unconfined case, we find a spatial separation of strains within the bulk of the hydrogel as strain becomes localized to an outer region. We also explore the contact mechanics of the gel, finding a transition from Hertzian behavior to non-Hertzian behavior as a function of swelling. Our model, implemented in the Morpho shape optimization environment, can be applied in any dimension, readily adapted to diverse swelling scenarios and extended to use other energies in conjunction.

cond-mat.soft↗

arXiv2022

Morpho -- A programmable environment for shape optimization and shapeshifting problems

An emerging theme across many domains of science and engineering is materials that change shape, often dramatically. Determining their structure involves solving a shape optimization problem where a given energy functional is minimized with respect to the shape of the domain and auxiliary fields describing the structure. Such problems are very challenging to solve and there is a lack of suitable simulation tools that are both readily accessible and general purpose. To address this gap, we present Morpho, an open-source programmable environment, and demonstrate its versatility by showcasing three applications to different areas of soft matter - swelling hydrogels, complex fluids that form aspherical droplets, to soap films and membranes - and advise on broader uses.

cond-mat.soft↗

arXiv2022

Methods for Eliciting Informative Prior Distributions: A Critical Review

Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While popular methods rely on asking experts probability based questions to quantify uncertainty, these methods are not without their drawbacks and many alternative elicitation methods exist. This paper explores methods for eliciting informative priors categorized by type and briefly discusses their strengths and limitations. Most of the review literature in this field focuses on a particular type of elicitation approach. The primary aim of this work, however, is to provide a more complete yet macro view of the state of the art by highlighting new (and old) approaches in one clear, easy to read article. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods; one of which, highlights a challenge that has not been addressed in the literature yet. We identify some of the gaps in the present work and discuss directions for future research.

stat.ME↗

arXiv2022

Data-driven discovery of active nematic hydrodynamics

Two-dimensional active nematics are often modeled using phenomenological continuum theories that describe the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistically accurate description of the experiments, the identification of the relevant terms in the PDEs and their parameters is usually indirect. Here, we adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from the spatio-temporal director and velocity data, via sparse fitting of the coarse-grained fields onto generic low order PDEs. We test the method extensively on computational models, and then apply it to data from experiments on microtubule-based active nematics. Thereby, we identify the optimal models for microtubule-based active nematics, along with the relevant phenomenological parameters. We find that the dynamics of the orientation field are largely governed by its coupling to the underlying flow, with free-energy gradients playing a negligible role. Furthermore, by fitting the flow equation to experimental data, we estimate a key parameter quantifying the `activity' of the nematic.

cond-mat.soft↗

arXiv2020

Exploiting Ultralow Loss Multimode Waveguides for Broadband Frequency Combs

Low propagation loss in high confinement waveguides is critical for chip-based nonlinear photonics applications. Sophisticated fabrication processes which yield sub-nm roughness are generally needed to reduce scattering points at the waveguide interfaces in order to achieve ultralow propagation loss. Here, we show ultralow propagation loss by shaping the mode using a highly multimode structure to reduce its overlap with the waveguide interfaces, thus relaxing the fabrication processing requirements. Microresonators with intrinsic quality factors (Q) of 31.8 $\pm$ 4.4 million are experimentally demonstrated. Although the microresonators support 10 transverse modes only the fundamental mode is excited and no higher order modes are observed when using nonlinear adiabatic bends. A record-low threshold pump power of 73 $μ$W for parametric oscillation is measured and a broadband, almost octave spanning single-soliton frequency comb without any signatures of higher order modes in the spectrum spanning from 1097 nm to 2040 nm (126 THz) is generated in the multimode microresonator. This work provides a design method that could be applied to different material platforms to achieve and use ultrahigh-Q multimode microresonators.

physics.optics↗

arXiv2020

Machine Learning Forecasting of Active Nematics

Active nematics are a class of far-from-equilibrium materials characterized by local orientational order of force-generating, anisotropic constitutes. Traditional methods for predicting the dynamics of active nematics rely on hydrodynamic models, which accurately describe idealized flows and many of the steady-state properties, but do not capture certain detailed dynamics of experimental active nematics. We have developed a deep learning approach that uses a Convolutional Long-Short-Term-Memory (ConvLSTM) algorithm to automatically learn and forecast the dynamics of active nematics. We demonstrate our purely data-driven approach on experiments of 2D unconfined active nematics of extensile microtubule bundles, as well as on data from numerical simulations of active nematics.

cond-mat.soft↗

arXiv2020

Considerations for developing predictive models of crime and new methods for measuring their accuracy

Developing spatio-temporal crime prediction models, and to a lesser extent, developing measures of accuracy and operational efficiency for them, has been an active area of research for almost two decades. Despite calls for rigorous and independent evaluations of model performance, such studies have been few and far between. In this paper, we argue that studies should focus not on finding the one predictive model or the one measure that is the most appropriate at all times, but instead on careful consideration of several factors that affect the choice of the model and the choice of the measure, to find the best measure and the best model for the problem at hand. We argue that because each problem is unique, it is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand. We develop a new measure called the penalized predictive accuracy index (PPAI) which imparts such flexibility. We also propose the use of the expected utility function to combine multiple measures in a way that is appropriate for a given problem in order to assess the models against multiple criteria. We further propose the use of the average logarithmic score (ALS) measure that is appropriate for many crime models and measures accuracy differently than existing measures. These measures can be used alongside existing measures to provide a more comprehensive means of assessing the accuracy and potential utility of spatio-temporal crime prediction models.

stat.AP↗

arXiv2020

Duality between Approximate Bayesian Methods and Prior Robustness

In this paper we show that there is a link between approximate Bayesian methods and prior robustness. We show that what is typically recognized as an approximation to the likelihood, either due to the simulated data as in the Approximate Bayesian Computation (ABC) methods or due to the functional approximation to the likelihood, can instead also be viewed upon as an implicit exercise in prior robustness. We first define two new classes of priors for the cases where the sufficient statistics is available, establish their mathematical properties and show, for a simple illustrative example, that these classes of priors can also be used to obtain the posterior distribution that would be obtained by implementing ABC. We then generalize and define two further classes of priors that are applicable in very general scenarios; one where the sufficient statistics is not available and another where the likelihood is approximated using a functional approximation. We then discuss the interpretation and elicitation aspects of the classes proposed here as well as their potential applications and possible computational benefits. These classes establish the duality between approximate Bayesian inference and prior robustness for a wide category of Bayesian inference methods.

stat.ME↗

arXiv2020

Adversarial Risk Analysis for First-Price Sealed-Bid Auctions

Adversarial Risk Analysis (ARA) is an upcoming methodology that is considered to have advantages over the traditional decision theoretic and game theoretic approaches. ARA solutions for first-price sealed-bid (FPSB) auctions have been found but only under strong assumptions which make the model somewhat unrealistic. In this paper, we use ARA methodology to model FPSB auctions using more realistic assumptions. We define a new utility function that considers bidders' wealth, we assume a reserve price and find solutions not only for risk-neutral but also for risk-averse as well as risk-seeking bidders. We model the problem using ARA for non-strategic play and level-k thinking solution concepts.

stat.AP↗

arXiv2020

Visible nonlinear photonics via high-order-mode dispersion engineering

Over the past decade, remarkable advances have been realized in chip-based nonlinear photonic devices for classical and quantum applications in the near- and mid-infrared regimes. However, few demonstrations have been realized in the visible and near-visible regimes, primarily due to the large normal material group-velocity dispersion (GVD) that makes it challenging to phase match third-order parametric processes. In this paper, we show that exploiting dispersion engineering of higher-order waveguide modes provides waveguide dispersion that allows for small or anomalous GVD in the visible and near-visible regimes and phase matching of four-wave mixing processes. We illustrate the power of this concept by demonstrating in silicon nitride microresonators a near-visible modelocked Kerr frequency comb and a narrow-band photon-pair source compatible with Rb transitions. These realizations extend applications of nonlinear photonics towards the visible and near-visible regimes for applications in time and frequency metrology, spectral calibration, quantum information, and biomedical applications.

physics.optics↗

arXiv2020

Broadband Ultrahigh-Resolution chip-scale Scanning Soliton Dual-Comb Spectroscopy

We present a chip-scale scanning dual-comb spectroscopy (SDCS) approach for broadband ultrahigh-resolution spectral acquisition. SDCS uses Si3N4 microring resonators that generate two single soliton micro-combs spanning 37 THz (300 nm) on the same chip from a single 1550-nm laser, forming a high-mutual-coherence dual-comb. We realize continuous tuning of the dual-comb system over the entire optical span of 37.5 THz with high precision using integrated microheater-based wavelength trackers. This continuous wavelength tuning is enabled by simultaneous tuning of the laser frequency and the two single soliton micro-combs over a full free spectral range of the microrings. We measure the SDCS resolution to be 319+-4.6 kHz. Using this SDCS system, we perform the molecular absorption spectroscopy of H13C14N over its 2.3 THz (18 nm)-wide overtone band, and show that the massively parallel heterodyning offered by the dual-comb expands the effective spectroscopic tuning speed of the laser by one order of magnitude. Our chip-scale SDCS opens the door to broadband spectrometry and massively parallel sensing with ultrahigh spectral resolution.

physics.app-ph↗