arXiv subjects

Sanket Patil

Publications and source records attributed to Sanket Patil.

7 recordsLinked to original sources

arXiv2026

AGNI: A differentiable MHD stability solver & optimizer for magnetic confinement fusion devices

The existence of an ideal MagnetoHydroDynamic (MHD) equilibrium does not guarantee its stability. Finite toroidal mode number (n) instabilities degrade performance in both tokamaks and stellarators and differentiable stability optimization tools to date have operated only in the infinite-n limit. We present AGNI (Analysis of Global Normal modes in Ideal MHD), a GPU-accelerated, automatically differentiable finite-n ideal MHD stability solver and optimizer. AGNI discretizes the ideal MHD energy principle pseudospectrally in real space using differentiation matrices and geometric coefficients from a DESC equilibrium, giving a variational eigenvalue problem for the plasma displacement, and efficiently finds the most unstable modes. Built on JAX, AGNI yields reverse-mode gradients of the growth rate with respect to boundary-shape and profile parameters without re-solving the equilibrium. We benchmark AGNI against the initial-value code NIMSTELL for a modified Landreman-Buller-Drevlak quasi-helically symmetric equilibrium, recovering the dominant m = n = 4 interchange mode with agreement in both growth rate and eigenfunction structure, and verify the automatic differentiation gradients against central finite differences. We quantify CPU and GPU cost for eigenvalue and gradient evaluation, establish the finite-precision limit on resolving near-marginal eigenvalues, and present a robust scheme to impose incompressibility compatible with gradient-based optimization. AGNI will allow us to optimize tokamaks, stellarators, and mirrors against ideal MHD instabilities.

physics.plasm-ph↗

arXiv2025

Robust Procurement: Bayesian Design under Worst-Case Approval Constraints

We study optimal procurement when a Bayesian designer must obtain approval from a non-Bayesian authority that shares the designer's objective but is uncertain about the value of the good and the supplier's cost. The designer uses a conjectured model to compute expected payoffs but is constrained to select among mechanisms delivering the largest payoff guarantee to the authority. This robustness requirement reshapes the tradeoff between efficiency and rent extraction: it reduces procurement from sellers with intermediate costs but may increase it from those with a high cost. When the good is sold in a market, we show that quantity regulation dominates price regulation if markups under the conjectured model are large, whereas price regulation dominates when demand uncertainty is substantial.

econ.TH↗

arXiv2025

A Novel Multimodal Framework for Early Detection of Alzheimers Disease Using Deep Learning

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in its early diagnosis, often leading to delayed treatment and poorer outcomes for patients. Traditional diagnostic methods, typically reliant on single data modalities, fall short of capturing the multifaceted nature of the disease. In this paper, we propose a novel multimodal framework for the early detection of AD that integrates data from three primary sources: MRI imaging, cognitive assessments, and biomarkers. This framework employs Convolutional Neural Networks (CNN) for analyzing MRI images and Long Short-Term Memory (LSTM) networks for processing cognitive and biomarker data. The system enhances diagnostic accuracy and reliability by aggregating results from these distinct modalities using advanced techniques like weighted averaging, even in incomplete data. The multimodal approach not only improves the robustness of the detection process but also enables the identification of AD at its earliest stages, offering a significant advantage over conventional methods. The integration of biomarkers and cognitive tests is particularly crucial, as these can detect Alzheimer's long before the onset of clinical symptoms, thereby facilitating earlier intervention and potentially altering the course of the disease. This research demonstrates that the proposed framework has the potential to revolutionize the early detection of AD, paving the way for more timely and effective treatments

cs.LG↗

arXiv2024

Undominated monopoly regulation

We study undominated mechanisms with transfers for regulating a monopolist who privately observes the marginal cost of production. We show that in any undominated mechanism, there is a quantity floor, which depends only on the primitives, and the regulator's operation decision is stochastic only if the monopolist produces at the quantity floor. We provide a near-complete characterization of the set of undominated mechanisms and use it to (a) provide a foundation for deterministic mechanisms, (b) show that the efficient mechanism is dominated, and (c) derive a max-min optimal regulatory mechanism.

econ.TH↗

arXiv2022

Investigating the effects of electron bounce-cyclotron resonance on plasma dynamics in capacitive discharges operated in the presence of a weak transverse magnetic field

Recently, S Patil et al. have reported the existence of an enhanced operating regime when a low-pressure (5 mTorr) capacitively coupled discharge (CCP) is driven by a very high radio-frequency (60 MHz) source in the presence of a weak external magnetic field applied parallel to its electrodes. Their Particle-in-Cell (PIC) simulations show, that a significantly higher bulk plasma density and ion flux can be achieved at the electrode when the electron cyclotron frequency equals half of the applied RF frequency for a given fixed voltage. In the present work we take a detailed look at this phenomenon and further delineate the effect of this "electron bounce cyclotron resonance (EBCR)" on the electron and ion dynamics of the system. We find that the ionization collision rate and stochastic heating is maximum under resonance condition. The electron energy distribution function also indicates that the population of tail end electrons is highest for the case where EBCR is maximum. Formation of electric field transients in the bulk plasma region are also seen at lower values of applied magnetic field. Finally, we demonstrate that the EBCR induced effect is a low pressure phenomenon and weakens as the neutral gas pressure increases. The potential utility of this effect to advance the operational performance of CCP devices for industrial purposes is discussed.

physics.plasm-ph↗

arXiv2020

An enhanced operating regime for high frequency capacitive discharges

We report the existence of an enhanced operating regime for a high-frequency, low-pressure capacitively coupled plasma (CCP) discharge in the presence of a weak magnetic field applied parallel to the electrodes. Our PIC-MCC simulations show that the plasma density and ion flux values exhibit a sharp peak when the electron cyclotron frequency equals half of the applied RF frequency. The physical mechanism responsible for this behaviour is traced to a synchronization between the oscillatory motion of the electrode sheath edge and the motion of a set of electrons reflected by this sheath. These electrons gain a substantial amount of energy that causes a concomitant higher ionisation leading to a peak in the ion flux. Our theoretical findings should be easy to verify experimentally in present day CCP devices and could provide useful guidelines for enhancing the operational performance of CCP devices in industrial applications.

physics.plasm-ph↗

arXiv2010

Providing Scalable Data Services in Ubiquitous Networks

Topology is a fundamental part of a network that governs connectivity between nodes, the amount of data flow and the efficiency of data flow between nodes. In traditional networks, due to physical limitations, topology remains static for the course of the network operation. Ubiquitous data networks (UDNs), alternatively, are more adaptive and can be configured for changes in their topology. This flexibility in controlling their topology makes them very appealing and an attractive medium for supporting "anywhere, any place" communication. However, it raises the problem of designing a dynamic topology. The dynamic topology design problem is of particular interest to application service providers who need to provide cost-effective data services on a ubiquitous network. In this paper we describe algorithms that decide when and how the topology should be reconfigured in response to a change in the data communication requirements of the network. In particular, we describe and compare a greedy algorithm, which is often used for topology reconfiguration, with a non-greedy algorithm based on metrical task systems. Experiments show the algorithm based on metrical task system has comparable performance to the greedy algorithm at a much lower reconfiguration cost.

cs.DB↗