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Influence of gauges in the numerical simulation of the time-dependent Ginzburg-Landau model arxiv.org/abs/2408.16086

Influence of gauges in the numerical simulation of the time-dependent Ginzburg-Landau model

The time-dependent Ginzburg-Landau (TDGL) model requires the choice of a gauge for the problem to be mathematically well-posed. In the literature, three gauges are commonly used: the Coulomb gauge, the Lorenz gauge and the temporal gauge. It has been noticed [J. Fleckinger-Pellé et al., Technical report, Argonne National Lab. (1997)] that these gauges can be continuously related by a single parameter considering the more general $ω$-gauge, where $ω$ is a non-negative real parameter. In this article, we study the influence of the gauge parameter $ω$ on the convergence of numerical simulations of the TDGL model using finite element schemes. A classical benchmark is first analysed for different values of $ω$ and artefacts are observed for lower values of $ω$. Then, we relate these observations with a systematic study of convergence orders in the unified $ω$-gauge framework. In particular, we show the existence of a tipping point value for $ω$, separating optimal convergence behaviour and a degenerate one. We find that numerical artefacts are correlated to the degeneracy of the convergence order of the method and we suggest strategies to avoid such undesirable effects. New 3D configurations are also investigated (the sphere with or without geometrical defect).

arxiv.org

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study arxiv.org/abs/2408.16087

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study

Bilevel optimization has witnessed a resurgence of interest, driven by its critical role in trustworthy and efficient machine learning applications. Recent research has focused on proposing efficient methods with provable convergence guarantees. However, while many prior works have established convergence to stationary points or local minima, obtaining the global optimum of bilevel optimization remains an important yet open problem. The difficulty lies in the fact that unlike many prior non-convex single-level problems, this bilevel problem does not admit a ``benign" landscape, and may indeed have multiple spurious local solutions. Nevertheless, attaining the global optimality is indispensable for ensuring reliability, safety, and cost-effectiveness, particularly in high-stakes engineering applications that rely on bilevel optimization. In this paper, we first explore the challenges of establishing a global convergence theory for bilevel optimization, and present two sufficient conditions for global convergence. We provide algorithm-specific proofs to rigorously substantiate these sufficient conditions along the optimization trajectory, focusing on two specific bilevel learning scenarios: representation learning and data hypercleaning (a.k.a. reweighting). Experiments corroborate the theoretical findings, demonstrating convergence to global minimum in both cases.

arxiv.org

Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps arxiv.org/abs/2408.15286

Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, we show that the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Pre-computation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle.

arxiv.org
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