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Optics of 3D Second-Harmonic Photonic Crystals from Colloidal Self-Assembly Templating
Item type: Conference Paper
Kainz T.; Talts L.; Grange R.; et al. (2026)
We present optical measurements on inverse opals with controlled second-harmonic generation within a fabricationtunable photonic stopband. We create scalable, 3D, second-order photonic crystals with unprecedented domain sizes (over 100 unit cells), overcoming previous limitations by combining colloidal self-assembly of polystyrene opals for templating and sol-gel barium titanate chemistry for replication.
Hyperbolic Super-Resonance and Long-Range Quantum Entanglement
Item type: Conference Paper
Narimanov E.E.; Demler E.A. (2026)
Hyperbolic materials support extreme momentum electromagnetic modes that enable deeply subwavelength field confinement and reshape light–matter interaction. We report the discovery of hyperbolic super-resonance, arising from clustered high-wavenumber modes in finite hyperbolic structures, which enables strong and even ultrastrong light–matter coupling and mediates strong long-range interaction between quantum emitters.
Exploring the coupling of TPMSs: What utility can they provide?
Item type: Conference Paper
Mondoño A.C.; Bergamini A. (2026)
Triply Periodic Minimal Surfaces (TPMS) offer lightweight, high-symmetry architectures with tunable mechanical properties. This study computationally investigates deformation coupling in TPMS-based beams under static and dynamic loading. Results reveal morphology-dependent behaviors, with sheet gyroids exhibiting strong bend-twist coupling and solid gyroids showing pull-twist responses. Coupling can be tailored via geometric modifications, enabling multifunctional, shapeadaptive structural design.
Model updating for bridge structures using reduced-order models and deep reinforcement learning
Item type: Conference Paper
Bruno G.; Parisi F.; Ruggieri S.; et al. (2026)
The continuous monitoring of existing bridges is a critical task for transportation agencies, which must assess the structural health of assets within a road network to prevent potential failures. To maximize the benefits of monitoring campaigns, it is often necessary to establish a calibrated numerical model of the structure, which should be capable of capturing structural changes due to damage or aging over time. This requires a model updating process, wherein uncertain parameters, such as material properties, boundary conditions, or internal constraints, are adjusted to align numerical predictions with experimental data. However, traditional model updating procedures, which rely on Full Order FE models, can become computationally expensive and highly reliant on expert input, particularly when the number of uncertain parameters is large. To address these challenges, this paper introduces a novel model updating framework that combines reduced-order modelling with deep reinforcement learning (DRL). The core idea is to train an intelligent agent capable of autonomously tuning a high-dimensional parameter space. A reduced-order representation of the structural model is first developed to significantly lower the computational cost of training the DRL agent while preserving key dynamic features. The proposed approach is detailed, its advantages and limitations are discussed, and its performance is demonstrated on a real-life reinforced concrete bridge.
Levitation optomechanics with meta-atoms
Item type: Conference Paper
Afridi A.; de Melo B.; Lu B.; et al. (2026)
We discuss the use of meta-optics in levitated optomechanics. By replacing silica nanoparticles with resonant silicon meta-atoms, we achieve enhanced optomechanical performance and deterministic control over optical forces. We show that engineering multipolar Mie resonances allows for switching between attractive and repulsive forces, directly mirroring the behavior of two-level systems.