arXiv subjects

Prateek Gupta

Publications and source records attributed to Prateek Gupta.

At least 37 records · Page 2Linked to original sources

arXiv2023

Adaptive friends-of-friends algorithm for identifying gravitationally bound cosmological structures

The Universe at the present epoch is found to be a network of matter over-dense and under-dense regions. To date, this picture of the Universe is best revealed through cosmological large-volume simulations and large-scale galaxy redshift surveys, in which, the most important step is the appropriate identification of structures. So far, these structures are identified using various group finding codes, mostly based on the friends-of-friends (FoF) or spherical over-density (SO) algorithms. Although, the main purpose is to identify gravitationally bound structures, surprisingly, the mass information has hardly been used effectively by these codes. Moreover, the methods used so far either constrain the over-density or use the real unstructured geometry only. Even though these are key factors in the accurate determination of structures-mass information, hardly any attempt has been made as yet to consider these important parameters together while formulating the grouping algorithms. In this paper, we present our proposed algorithm which takes care of all the above-mentioned relevant features and ensures the bound structures by means of physical quantities, mainly mass and the total energy information. We introduced a novel concept of physically relevant arm-length for each element depending on their individual gravity leading to a distinct linking length for each unique pair of elements. This proposed algorithm is thus fundamentally new that, not only able to catch the gravitationally bound, real unstructured geometry, it does identify it roughly within a predefined physically motivated density threshold. Such a thing could not be simultaneously achieved before by any of the usual FoF or SO-based methods. We also demonstrate the unique ability of the code in the appropriate identification of structures, both from large volume cosmological simulations as well as from galaxy redshift surveys.

astro-ph.CO↗

arXiv2023

AI For Global Climate Cooperation 2023 Competition Proceedings

The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.

cs.AI↗

arXiv2023

Baroclinic interaction of forced shock waves with random thermal gradients

Density gradients aligned at an angle to pressure gradients result in baroclinic torque in fluid flows, generating vorticity. In this work, we study the vorticity generated by the baroclinic torque exerted by the interaction of pressure jumps across random two-dimensional shock waves with density gradients. A field of random two-dimensional shock waves has acoustic spectral energy scaling as ε^{2/3}{\ell}^{-1/3}k^{-2} where k is the wavenumber, ε is the energy dissipation, and \ell is the integral length scale of the field. Since the acoustic energy is broadband, pressure and velocity gradients exist in a wide range of length scales. We study the interaction of these broadband gradients with isobaric thermal gradients localized at a length scale in the spectral space. We show that the method of generating shock waves or injection of wave energy in the system governs the baroclinic interactions. For stochastically forced shock waves, baroclinic termsare negligible. Broadband vorticity with energy at least two orders of magnitude smaller is generated due to continuous variation in curvature of shock waves caused by stochastic forcing. On the other hand, shock waves maintained by energy rescaling result in the generation of coherent vorticity. We also discuss the relative magnitude of the baroclinic torque generated due to total density gradients compared to the one generated due to non-isentropic density gradients within the shock waves interacting with the pressure gradients.

physics.flu-dyn↗

arXiv2023

GNN-Assisted Phase Space Integration with Application to Atomistics

Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations -- especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, we highlight the impact on time-coarsened atomistic predictions, and we discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics.

physics.comp-ph↗

arXiv2022

Lookback for Learning to Branch

The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the "lookback" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple "answer". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.

cs.LG↗

arXiv2022

Ship Performance Monitoring using Machine-learning

The hydrodynamic performance of a sea-going ship varies over its lifespan due to factors like marine fouling and the condition of the anti-fouling paint system. In order to accurately estimate the power demand and fuel consumption for a planned voyage, it is important to assess the hydrodynamic performance of the ship. The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data. Three ML methods, NL-PCR, NL-PLSR and probabilistic ANN, are calibrated using the data from two sister ships. The calibrated models are used to extract the varying trend in ship's hydrodynamic performance over time and predict the change in performance through several propeller and hull cleaning events. The predicted change in performance is compared with the corresponding values estimated using the fouling friction coefficient ($ΔC_F$). The ML methods are found to be performing well while modelling the hydrodynamic state variables of the ships with probabilistic ANN model performing the best, but the results from NL-PCR and NL-PLSR are not far behind, indicating that it may be possible to use simple methods to solve such problems with the help of domain knowledge.

stat.ML↗

arXiv2022

Effect of confinement on flow around a rotating elliptic cylinder in laminar flow regime

The flow phenomena around a rotating elliptic cylinder in a channel is studied numerically. The value of the confinement parameter βis varied as \frac{1}{k}, where k = 2, 4, 6, and 8 respectively, to demonstrate the vortex-shedding patterns around the cylinder in the downstream wake. The non-dimensional rotation rate αtakes up 0.5, 1, and 2 as its value. Additionally, the Reynolds number (\textit{Re}) based on the cylinder diameter is taken to be 50, 100, and 150 respectively. A parametric study is performed to explain the changes in drag coefficient \textit{(C_{D})}, lift coefficient \textit{(C_{L})}, and moment coefficient \textit{(C_{M})} with variations of β, α, and \textit{Re}. The Fast-Fourier transform (FFT) of the time-periodic lift signals is presented to understand the shedding frequency characteristics, and the \textit{C_{M}} values are analyzed for cases of autorotation. Despite the introduction of significant confinement and cylinder rotation, complete suppression of vortex shedding is not observed for the considered parameter space. Autorotation is observed and becomes prominent with decrease in non-dimensional rotation rate and increase in confinement and Reynolds number.

physics.flu-dyn↗

arXiv2022

Exploring diffuse radio emission in galaxy clusters and groups with the uGMRT and the SKA

Diffuse radio emission has been detected in a considerable number of galaxy clusters and groups, revealing the presence of pervasive cosmic magnetic fields, and of relativistic particles in the large-scale structure (LSS) of the Universe. Since cluster radio emission is faint and steep spectrum, its observations are largely limited by the instrument sensitivity and frequency of observation, leading to a dearth of information, more so for lower-mass systems. The unprecedented sensitivity of recently commissioned low-frequency radio telescope arrays, aided by the development of advanced calibration and imaging techniques, have helped in achieving unparalleled image quality. At the same time, the development of sophisticated numerical simulations and the availability of supercomputing facilities have paved the way for high-resolution numerical modeling of radio emission, and the structure of the cosmic magnetic fields in LSS, leading to predictions matching the capabilities of observational facilities. In view of these rapidly-evolving scenerio in modeling and observations, in this review, we summarise the role of the new telescope arrays and the development of advanced imaging techniques and discuss the detections of various kinds of cluster radio sources. In particular, we discuss observations of the cosmic web in the form of supercluster filaments, studies of emission in poor clusters and groups of galaxies, and of ultra-steep spectrum sources. We also review the current theoretical understanding of various diffuse cluster radio sources and the associated magnetic field and polarization. As the statistics of detections improve along with our theoretical understanding, we update the source classification schemes based on their intrinsic properties. We conclude by summarising the role of the upgraded GMRT and our expectations from the upcoming Square Kilometre Array (SKA) observatories.

astro-ph.CO↗

arXiv2022

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpinsAI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

cs.LG↗

arXiv2022

(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment

Insufficient Social Cost of Carbon (SCC) estimation methods and short-term decision-making horizons have hindered the ability of carbon emitters to properly correct for the negative externalities of climate change, as well as the capacity of nations to balance economic and climate policy. To overcome these limitations, we introduce Retrospective Social Cost of Carbon Updating (ReSCCU), a novel mechanism that corrects for these limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP), a market mechanism in which polluters offload the payment of ReSCCU adjustments to insurers. To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market, which could see real-world implementation based on the engagement of a few high net-worth individuals or independent institutions.

cs.CY↗

arXiv2022

Finite-temperature surface elasticity of crystalline solids

Surface energies and surface elasticity largely affect the mechanical response of nanostructures as well as the physical phenomenon associated with surfaces such as evaporation and adsorption. Studying surface energies at finite temperatures is therefore of immense interest for nanoscale applications. However, calculating surface energies and derived quantities from atomistic ensembles is usually limited to zero temperature or involve cumbersome thermodynamic integration techniques at finite temperature. Here, we illustrate a technique to identify the energy and elastic properties of surfaces of solids at non-zero temperature based on a Gaussian phase packets (GPP) approach (which in the isothermal limit coincides with a maximum-entropy formulation). Using this setup, we investigate the effect of temperature on the surface properties of different crystal faces for six pure metals -- copper, nickel, alumimum, iron, tungsten and vanadium -- thus covering both FCC and BCC lattice structures. While the obtained surface energies and stresses usually show a decreasing trend with increasing temperature, the elastic constants do not show such a consistent trend across the different materials and are quite sensitive to temperature changes. Validation is performed by comparing the obtained surface energy densities of selected BCC and FCC materials to those calculated via molecular dynamics.

cond-mat.mtrl-sci↗

arXiv2022

Data Processing Framework for Ship Performance Analysis

The hydrodynamic performance of a sea-going ship can be analysed using the data obtained from the ship. Such data can be gathered from different sources, like onboard recorded in-service data, AIS data, and noon reports. Each of these sources is known to have their inherent problems. The current work gives a brief introduction to these data sources as well as the common problems associated with them, along with some examples. In order to resolve most of these problems, a streamlined semi-automatic data processing framework for fast data processing is developed and presented here. The data processing framework can be used to process the data obtained from any of the above three mentioned sources. The framework incorporates processing steps like interpolating weather hindcast (metocean) data to ship's location in time, deriving additional features, validating data, estimating resistance components, data cleaning, and outlier detection. A brief description of each of the processing steps is provided with examples from existing datasets. The processed data can be further used to analyse the hydrodynamic performance of a ship.

cs.DB↗

arXiv2021

uGMRT detection of cluster radio emission in low-mass Planck~SZ clusters

Low-mass ($M_{\rm{500}}<5\times10^{14}{\rm{M_\odot}}$) galaxy clusters have been largely unexplored in radio observations, due to the inadequate sensitivity of existing telescopes. However, the upgraded GMRT (uGMRT) and the Low Frequency ARray (LoFAR), with unprecedented sensitivity at low frequencies, have paved the way to closely study less massive clusters than before. We have started the first large-scale programme to systematically search for diffuse radio emission from low-mass galaxy clusters, chosen from the Planck Sunyaev-Zel'dovich cluster catalogue. We report here the detection of diffuse radio emission from four of the 12 objects in our sample, shortlisted from the inspection of the LoFAR Two Meter Sky Survey (LoTSS-I), followed up by uGMRT Band 3 deep observations. The clusters PSZ2~G089 (Abell~1904) and PSZ2~G111 (Abell~1697) are detected with relic-like emission, while PSZ2~G106 is found to have an intermediate radio halo and PSZ2~G080 (Abell~2018) seems to be a halo-relic system. PSZ2~G089 and PSZ2~G080 are among the lowest-mass clusters discovered with a radio relic and a halo-relic system, respectively. A high ($\sim30\%$) detection rate, with powerful radio emission ($P_{1.4\ {\rm GHz}}\sim10^{23}~{\rm{W~Hz^{-1}}}$) found in most of these objects, opens up prospects of studying radio emission in galaxy clusters over a wider mass range, to much lower-mass systems.

astro-ph.GA↗

arXiv2021

Theoretical and Numerical Investigation of Nonlinear Thermoacoustic, Acoustic, and Detonation Waves

We begin with the theoretical study of spectral energy cascade due to the propagation of high amplitude sound in the absence of thermal sources. To this end, a first-principles-based system of governing equations, correct up to second order in perturbation variables is derived. The exact energy corollary of such second-order system of equations is then formulated and used to elucidate the spectral energy dynamics of nonlinear acoustic waves. We then extend this analysis to thermoacoustically unstable waves -- i.e. amplified as a result of thermoacoustic instability. We drive such instability up until the generation of shock waves. We further study the nonlinear wave propagation in geometrically complex case of waves induced by the spark plasma between the electrodes. This case adds the geometrical complexity of a curved, three-dimensional shock, yielding vorticity production due to baroclinic torque. Finally, detonation waves are simulated by using a low-order approach, in a periodic setup subjected to high pressure inlet and exhaust of combustible gaseous mixture. An order adaptive fully compressible and unstructured Navier Stokes solver is currently under development to enable higher fidelity studies of both the spark plasma and detonation wave problem in the future.

physics.flu-dyn↗

arXiv2021

Nonequilibrium thermomechanics of Gaussian phase packet crystals: application to the quasistatic quasicontinuum method

The quasicontinuum method was originally introduced to bridge across length scales -- from atomistics to significantly larger continuum scales -- thus overcoming a key limitation of classical atomic-scale simulation techniques while solely relying on atomic-scale input (in the form of interatomic potentials). An associated challenge lies in bridging across time scales to overcome the time scale limitations of atomistics. To address the biggest challenge, bridging across both length and time scales, only a few techniques exist, and most of those are limited to conditions of constant temperature. Here, we present a new strategy for the space-time coarsening of an atomistic ensemble, which introduces thermomechanical coupling. We investigate the quasistatics and dynamics of a crystalline solid described as a lattice of lumped correlated Gaussian phase packets occupying atomic lattice sites. By definition, phase packets account for the dynamics of crystalline lattices at finite temperature through the statistical variances of atomic momenta and positions. We show that momentum-space correlation allows for an exchange between potential and kinetic contributions to the crystal's Hamiltonian. Consequently, local adiabatic heating due to atomic site motion is captured. Moreover, within the quasistatic approximation the governing equations reduce to the minimization of thermodynamic potentials such as Helmholtz free energy (depending on the fixed variables), and they yield the local equation of state. We further discuss opportunities for describing atomic-level thermal transport using the correlated Gaussian phase packet formulation and the importance of interatomic correlations. Such a formulation offers a promising avenue for a finite-temperature non-equilibrium quasicontinuum method that may be combined with thermal transport models.

cond-mat.mes-hall↗

arXiv2020

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

cs.CY↗

arXiv2020

Hybrid Models for Learning to Branch

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-layer perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on. The code for this project is publicly available at https://github.com/pg2455/Hybrid-learn2branch.

cs.LG↗

arXiv2020

Predicting Infectiousness for Proactive Contact Tracing

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

cs.LG↗