Deep learning-assisted modeling for χ(2) nonlinear optics

Advanced Photonics · 2026-05-06

Macroscopic Traffic Flow Network Modeling For Wildfire Evacuation: A Game-Theoretic Junction Optimization Approach with Application to Lahaina Fire

ArXiv.org · 2026-03-30

The 2023 Lahaina wildfire killed 102 people on a peninsula served by a single two-lane highway, making exit lane capacity the binding constraint on evacuation time. We model the evacuation as a system of hyperbolic scalar conservation laws on a directed graph with game-theoretic junction conditions that maximize total network flux, an evacuation-calibrated piecewise linear-quadratic flux function, and a loss-driven optimization framework that tunes traffic distribution toward priority corridors. Analytical results on a toy network and numerical simulations of the Lahaina road network reveal a phase transition in exit lane capacity. Additional lanes improve throughput linearly until a computable critical threshold, beyond which no route optimization yields further benefit. For Lahaina, reversing one southbound lane captures nearly all achievable improvement, and a fourth lane can be reserved for emergency vehicles with negligible impact on civilian clearance time. These results provide a rigorous mathematical basis for contraflow recommendations in wildland-urban interface evacuations.

Numerical analysis of a floating Airborne Wind Energy farm with shared-mooring

SSRN Electronic Journal · 2026-01-01

Macroscopic Traffic Flow Network Modeling For Wildfire Evacuation: A Game-Theoretic Junction Optimization Approach with Application to Lahaina Fire

arXiv (Cornell University) · 2026-03-30

The 2023 Lahaina wildfire killed 102 people on a peninsula served by a single two-lane highway, making exit lane capacity the binding constraint on evacuation time. We model the evacuation as a system of hyperbolic scalar conservation laws on a directed graph with game-theoretic junction conditions that maximize total network flux, an evacuation-calibrated piecewise linear-quadratic flux function, and a loss-driven optimization framework that tunes traffic distribution toward priority corridors. Analytical results on a toy network and numerical simulations of the Lahaina road network reveal a phase transition in exit lane capacity. Additional lanes improve throughput linearly until a computable critical threshold, beyond which no route optimization yields further benefit. For Lahaina, reversing one southbound lane captures nearly all achievable improvement, and a fourth lane can be reserved for emergency vehicles with negligible impact on civilian clearance time. These results provide a rigorous mathematical basis for contraflow recommendations in wildland-urban interface evacuations.

Structured Light at the Extreme: Harnessing Spatiotemporal Control for High-Field Laser-Matter Interactions

ArXiv.org · 2025-12-04 · 2 citations

This review charts the emerging paradigm of intelligent structured light for high-field laser-matter interactions, where the precise spatiotemporal and vectorial control of light is a critical degree of freedom. We outline a transformative framework built upon three synergistic pillars. First, we survey the advanced electromagnetic toolkit, moving beyond conventional spatial light modulators to include robust static optics and the promising frontier of plasma light modulators. Second, we detail the optimization engine for this high-dimensional design space, focusing on physics-informed digital twins and AI-driven inverse design to automate the discovery of optimal light structures. Finally, we explore the groundbreaking applications enabled by this integrated approach, including programmable electron beams, orbital-angular-momentum-carrying γ-rays, compact THz accelerators, and robust communications. The path forward necessitates overcoming grand challenges in material science, real-time adaptive control at MHz rates, and the extension of these principles to the quantum realm. This review serves as a call to action for a coordinated, interdisciplinary effort to command, rather than merely observe, light-matter interactions at the extreme.

Video: Transient Segregation Dynamics of Bidisperse Particle-Laden Flow on an Incline

2025-11-23

Mobile Sensor Placement for the 3-Coverage Problem in Confined Geometries

2025-11-12

We consider the 3-coverage problem in confined geometries using mobile sensors. Many sensor problems require the use of multiple cooperative sensors for geolocation of events (e.g. using sound for triangulation). Optimal placement of sensors can be challenging, especially when confined to unusual geometries. We propose a novel swarming algorithm involving pairwise interaction potentials. Such models have low-energy cooperative states in which the agents form local Voronoi-style hexagonal patterns. We focus on the Morse potential and show that under certain choices of parameters, particles reach a desirable uniform distribution of 3-coverage. Such properties include (a) maintaining optimal distance as sensors are added or removed, (b) ability to navigate with limited communication between sensors, such as near-neighbor interactions for the graph topology of the sensor network, and (c) ability to target specific areas within a confined geometry especially with limited sensor resources.

Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint

ArXiv.org · 2025-12-03

Flow matching-based generative models have been integrated into the plug-and-play image restoration framework, and the resulting plug-and-play flow matching (PnP-Flow) model has achieved some remarkable empirical success for image restoration. However, the theoretical understanding of PnP-Flow lags its empirical success. In this paper, we derive a continuous limit for PnP-Flow, resulting in a stochastic differential equation (SDE) surrogate model of PnP-Flow. The SDE model provides two particular insights to improve PnP-Flow for image restoration: (1) It enables us to quantify the error for image restoration, informing us to improve step scheduling and regularize the Lipschitz constant of the neural network-parameterized vector field for error reduction. (2) It informs us to accelerate off-the-shelf PnP-Flow models via extrapolation, resulting in a rescaled version of the proposed SDE model. We validate the efficacy of the SDE-informed improved PnP-Flow using several benchmark tasks, including image denoising, deblurring, super-resolution, and inpainting. Numerical results show that our method significantly outperforms the baseline PnP-Flow and other state-of-the-art approaches, achieving superior performance across evaluation metrics.

Machine learning techniques for frequency comb optimization

2025-03-19

We use supervised and unsupervised machine learning techniques to investigate how optical frequency combs may be used to identify gas molecules in the atmosphere. Dual comb heterodyne detection with GHz and THz repetition rates are often used as spectroscopic methods for probing ro-vibrational IR spectra of small molecules, proving to be promising in dispersive atmospheric sensing regimes. We simulate and recover an intensity spectrum from the molecule-specific IR absorption in the region spanned by the comb bandwidth. In practice, conventional frequency combs contain a narrow range of frequencies that may not span the spectral range associated with typical atmospheric target signals, prohibiting convenient molecule identification. We present several inexpensive, efficient machine learning methods to analyze optimal comb frequencies to maximize the ability to determine the molecular composition of a sample, based only on the interactions at those frequencies with respect to various channels of noise. Using a synthetic dataset, these methods are able to accurately identify molecules using simulated frequency combs, comparable to the ones used in practice. Furthermore, these methods withstand added noise (based on the imperfections of the experimental equipment) beyond what is expected in real applications. We also investigate the optimal comb generation parameters that provide the most informational value, and provide an analysis of the change in accuracy based on the number and size of allocated combs. The robustness of these methods suggests the techniques presented in this paper may be integrated into a laboratory setup and even in on-chip comb systems deployed in an atmospheric setting. This approach may be used as a detection framework for future quantum-enhanced systems via squeezed bi-photon comb inputs.

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability

Mathematical Biosciences · 2025-06-27 · 2 citations