相关论文: Application of Convolutional Neural Networks to Pr…

We adopt the deep learning method CASI (Convolutional Approach to Shell Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line spectra, such as 13CO. We adopt magneto-hydrodynamics…

Observing 3D magnetic fields, including orientation and strength, within the interstellar medium is vital but notoriously difficult. However, recent advances in our understanding of anisotropic magnetohydrodynamic (MHD) turbulence…

3D Galactic magnetic fields are critical for understanding the interstellar medium, Galactic foreground polarization, and the propagation of ultra-high-energy cosmic rays. Leveraging recent theoretical insights into anisotropic…

We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of…

We examine the role of the interstellar magnetic field to modulate the orientation of turbulent flows within the Taurus molecular cloud using spatial gradients of thin velocity slices of 12CO and 13CO antenna temperatures. Our analysis…

Optical and infrared polarization mapping and recent Planck observations of the filamentary cloud L1495 in Taurus show that the large-scale magnetic field is approximately perpendicular to the long axis of the cloud. We use the HAWC+…

Magnetically aligned velocity anisotropy over varying physical conditions and environments within the Taurus Molecular Cloud is evaluated from analysis of wide field spectroscopic imaging of 12CO and 13CO J=1-0 emission. Such anisotropy is…

We present a deep learning approach using an optical neural network to predict the fundamental modal indices $n_{\rm{eff}}$ in a silicon (Si) channel waveguide. We use three inputs, e.g., two geometric parameters and one material property,…

Galactic winds probe how stellar feedback regulates the mass and metallicity of galaxies. Observations show that galactic winds are multiphase and magnetised. In the local Universe, the dense phase is traced by emission and absorption…

Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the…

High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others. The rising popularity of high-fidelity…

This paper presents a machine learning methodology to improve the predictions of traditional RANS turbulence models in channel flows subject to strong variations in their thermophysical properties. The developed formulation contains several…

Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep learning technique that has proven to be the most successful in…

At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric measurements such as the CAS system have thus become an…

To better address challenging issues of the irregularity and inhomogeneity inherently present in 3D point clouds, researchers have been shifting their focus from the design of hand-craft point feature towards the learning of 3D point…

Despite the plethora of deep learning frameworks for flow field prediction, most of them deal with flow fields on regular domains, and although the best ones can cope with irregular domains, they mostly rely on graph networks, so that real…

We present synthetic dust polarization maps of simulated molecular clouds (MCs) with the goal to systematically explore the origin of the relative orientation of the magnetic field ($\bf{B}$) with respect to the MC sub-structures identified…

Super-Alfv\'enic turbulence is widespread in astrophysical environments, including molecular clouds and the diffuse plasma of galaxy clusters. At large scales, magnetic fields play only a minor dynamical role; however, for sufficiently…

Very short-term convective storm forecasting, termed nowcasting, has long been an important issue and has attracted substantial interest. Existing nowcasting methods rely principally on radar images and are limited in terms of nowcasting…

Clouds classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact on the variation of atmospheric conditions, with the…