arXiv subjects

Aydın Cem Keser

Publications and source records attributed to Aydın Cem Keser.

4 recordsLinked to original sources

arXiv2026

Statistical Landscape Exploration and Weak-Field Defects in Selective Oscillator Ising Machines

Oscillator Ising machines (OIMs) provide an analog dynamical approach to combinatorial optimization, but comparatively little is known about the statistical structure of the ensembles they generate. Using the Sherrington-Kirkpatrick (SK) spin-glass model, we introduce a selective oscillator scheme, OIM+, in which successive annealing epochs are combined with energy-based elimination and repopulation of replicas. We characterize the resulting states through overlap distributions, Hamming distances, hierarchical clustering, and local-field statistics. OIM+ explores structured low-energy regions while retaining a nontrivial ensemble of configurations despite repeated selection. We identify sparse weak-local-field residual defects as a characteristic limitation of the dynamics. A short Metropolis-Hastings refinement preferentially corrects these defects, indicating that the oscillator dynamics performs the dominant collective exploration while the remaining corrections are predominantly local. The weak fields arise mainly from cancellation among competing interaction terms rather than uniformly weak couplings. The same behavior persists for an antiferromagnetically biased SK ensemble, where the hybrid method reaches a pooled mean energy comparable to a substantially longer pure Metropolis anneal. The complete protocol also achieves competitive performance on standard G-set Max-Cut benchmarks. These results suggest a natural division of labor between collective analog search and targeted local refinement.

cond-mat.dis-nn↗

arXiv2026

MUTACO: Simulator-Efficient Response Matching in Stochastic Nonlinear Systems via Compatibility-Aware Sequential Design

Many physical inverse problems are mediated by expensive stochastic simulators for which response gradients, adjoint sensitivities, or a differentiable simulator interface are unavailable. We introduce Multiple Target-based Confident Acquisition (MUTACO), an uncertainty-aware sequential-design method for response matching that requires only noisy forward evaluations and learns the multicomponent response together with its predictive uncertainty. We develop and evaluate MUTACO for a disordered network of stochastic phase oscillators. The inverse task is to infer statistical properties of the local potential and interactions from macroscopic rotation and synchronization curves under multiple driving conditions. Rather than requiring unique recovery of these statistical properties, MUTACO searches for parameter configurations whose predicted rotation and synchronization responses are compatible with the target response while accounting for predictive uncertainty. This response-centered formulation is appropriate when experimentally accessible observables characterize ensemble statistics rather than a particular microscopic realization. Under strictly limited simulation budgets, MUTACO achieves the lowest held-out response error among the tested adaptive-search, space-filling, and simulation-based inference methods, while also yielding lower parameter-recovery error than the compared methods. These results demonstrate that response-targeted sequential design is a simulator-efficient approach to inverse modeling from finite noisy forward evaluations and suggest broader applicability to stochastic physical systems with correlated multicomponent responses and weakly constrained parameter directions.

cond-mat.stat-mech↗

arXiv2024

Driving Viscous Hydrodynamics in Bulk Electron Flow in Graphene Using Micromagnets

We consider the hydrodynamic flow of an electron fluid in a channel formed in a two-dimensional electron gas (2DEG) with no-slip boundary conditions. To generate vorticity in the fluid the flow is influenced by an array of micromagnets on the top of 2DEG. We analyse the viscous boundary layer and demonstrate anti-Poiseuille behaviour in this region. Furthermore we predict a longitudinal voltage modulation, where a periodic magnetic field generates a voltage term periodic in the direction of transport.From the experimental point of view we propose a method for a boundary-independent measurement of the viscosity of different electron fluids. The results are applicable to graphene away from the charge neutrality point and to semiconductors.

cond-mat.mes-hall↗

arXiv2021

Geometric control of universal hydrodynamic flow in a two dimensional electron fluid

Fluid dynamics is one of the cornerstones of modern physics and has recently found applications in the transport of electrons in solids. In most solids electron transport is dominated by extrinsic factors, such as sample geometry and scattering from impurities. However in the hydrodynamic regime Coulomb interactions transform the electron motion from independent particles to the collective motion of a viscous `electron fluid'. The fluid viscosity is an intrinsic property of the electron system, determined solely by the electron-electron interactions. Resolving the universal intrinsic viscosity is challenging, as it only affects the resistance through interactions with the sample boundaries, whose roughness is not only unknown but also varies from device to device. Here we eliminate all unknown parameters by fabricating samples with smooth sidewalls to achieve the perfect slip boundary condition, which has been elusive both in molecular fluids and electronic systems. We engineer the device geometry to create viscous dissipation and reveal the true intrinsic hydrodynamic properties of a 2D system. We observe a clear transition from ballistic to hydrodynamic electron motion, driven by both temperature and magnetic field. We directly measure the viscosity and electron-electron scattering lifetime (the Fermi quasiparticle lifetime) over a wide temperature range without fitting parameters, and show they have a strong dependence on electron density that cannot be explained by conventional theories based on the Random Phase Approximation.

cond-mat.mes-hall↗