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The Finite-Difference Time-Domain (FDTD) method is a numerical modeling technique used by researchers as one of the most accurate methods to simulate the propagation of an electromagnetic wave through an object over time. Due to the nature of the method, FDTD can be computationally expensive when used in complex setting such as light propagation in highly heterogenous object such as the imaging process of tissues. In this paper, we explore a Deep Learning (DL) model that predicts the evolution of an electromagnetic field in a heterogeneous medium. In particular, modeling for propagation of a Gaussian beam in skin tissue layers. This is relevant for the characterization of microscopy imaging of tissues. Our proposed model named FDTD-net, is based on the U-net architecture, seems to perform the prediction of the electric field (EF) with good accuracy and faster when compared to the FDTD method. A dataset of different geometries was created to simulate the propagation of the electric field. The propagation of the electric field was initially generated using the traditional FDTD method. This data set was used for training and testing of the FDTD-net. The experiments show that the FDTD-net learns the physics related to the propagation of the source in the heterogeneous objects, and it can capture changes in the field due to changes in the object morphology. As a result, we present a DL model that can compute a propagated electric field in less time than the traditional method. 

Abstract We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-αforest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range 0.1 <z< 4.2. To mitigate confirmation bias, a blind analysis was implemented to measure the BAO scales. DESI BAO data alone are consistent with the standard flat ΛCDM cosmological model with a matter density Ω_m=0.295±0.015. Paired with a baryon density prior from Big Bang Nucleosynthesis and the robustly measured acoustic angular scale from the cosmic microwave background (CMB), DESI requiresH₀=(68.52±0.62) km s^-1Mpc^-1. In conjunction with CMB anisotropies fromPlanckand CMB lensing data fromPlanckand ACT, we find Ω_m=0.307± 0.005 andH₀=(67.97±0.38) km s^-1Mpc^-1. Extending the baseline model with a constant dark energy equation of state parameterw, DESI BAO alone requirew=-0.99^+0.15_-0.13. In models with a time-varying dark energy equation of state parametrised byw₀andw_a, combinations of DESI with CMB or with type Ia supernovae (SN Ia) individually preferw₀> -1 andw_a< 0. This preference is 2.6σfor the DESI+CMB combination, and persists or grows when SN Ia are added in, giving results discrepant with the ΛCDM model at the 2.5σ, 3.5σor 3.9σlevels for the addition of the Pantheon+, Union3, or DES-SN5YR supernova datasets respectively. For the flat ΛCDM model with the sum of neutrino mass ∑m_νfree, combining the DESI and CMB data yields an upper limit ∑m_ν< 0.072 (0.113) eV at 95% confidence for a ∑m_ν> 0 (∑m_ν> 0.059) eV prior. These neutrino-mass constraints are substantially relaxed if the background dynamics are allowed to deviate from flat ΛCDM.