most citedForecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks

3 citations

5 papers

physics.optics2026

A single-step lithography process for reconfigurable SiN photonics with TiN heaters and Al interconnects

Leonardo Limongi, Rachele Favaretto, Lorenzo Baldessarini +6

Thermo-optic phase shifters are key building blocks in Silicon and Silicon Nitride-based reconfigurable photonic integrated circuits. They enable manipulating the phase of an optic…

cond-mat.dis-nn2026

Spectral properties of non-Hermitian real random matrices with long-range correlations

Ulysse Marquis

We investigate the spectral properties of non-Hermitian real random matrices whose entries exhibit long-range correlations decaying as~. We find a progressive breakdo…

physics.soc-ph20263 cited

Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks

Martina Rama, Gabriele Santin, Giulia Cencetti +2

Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While Physics-Informed Neural Networks have shown promise in various scien…

cond-mat.dis-nn2026

FKPP fronts in quenched random media

Ulysse Marquis, Henri Berestycki, Marc Barthelemy

We study numerically the evolution of one-dimensional FKPP fronts initiated from steep initial conditions in the presence of a quenched random growth rate. Compared to both the hom…

hep-ex2026

Sensitivity to low-mass WIMPs with an improved liquid argon ionization response model within the DarkSide programme

F. Acerbi, P. Adhikari, P. Agnes +294

Dark matter detection experiments using liquid argon rely on a precise characterization of the ionization response to nuclear recoils, especially in the keV energy range relevant f…