Guillaume Gachon | Searcharxiv

arXiv2026

Band Inversion Flips the Winding of Bound States in the Continuum

Bound states in the continuum (BICs) in photonic slabs and metasurfaces appear as polarization singularities in momentum space, characterized by an integer winding number. This winding is widely treated as a robust topological label, preserved under smooth deformations of the structure. Here we show that this robustness fails under band inversion. Using a general two-band theory of open periodic photonic structures, we prove that a band inversion at a band-edge BIC reverses the local far-field polarization map and flips the BIC winding, $w_{\rm BIC}\to -w_{\rm BIC}$, \emph{without} any defect dynamics in momentum space. We verify the prediction in a tunable subwavelength grating, where polarization tomography directly images the reversal, and confirm it numerically in multiband rectangular and triangular photonic lattices. Band inversion thus emerges as a key mechanism governing polarization-singularity topology in non-Hermitian photonic band structures.

physics.optics↗

arXiv2025

Learning to generate physical ocean states: Towards hybrid climate modeling

Ocean General Circulation Models require extensive computational resources to reach equilibrium states, while deep learning emulators, despite offering fast predictions, lack the physical interpretability and long-term stability necessary for climate scientists to understand climate sensitivity (to greenhouse gas emissions) and mechanisms of abrupt % variability such as tipping points. We propose to take the best from both worlds by leveraging deep generative models to produce physically consistent oceanic states that can serve as initial conditions for climate projections. We assess the viability of this hybrid approach through both physical metrics and numerical experiments, and highlight the benefits of enforcing physical constraints during generation. Although we train here on ocean variables from idealized numerical simulations, we claim that this hybrid approach, combining the computational efficiency of deep learning with the physical accuracy of numerical models, can effectively reduce the computational burden of running climate models to equilibrium, and reduce uncertainties in climate projections by minimizing drifts in baseline simulations.

cs.LG↗