相关论文: Disk2Planet: A Robust and Automated Machine Learni…
While it is generally accepted that the magnetic field and its non-ideal effects play important roles during the stellar formation, simple models of pure hydrodynamics and angular momentum conservation are still widely employed in the…
The topic of the present study is combining a dynamic model of a protoplanetary disk with the computations of radiation transfer for obtaining synthetic spectra and disk images suitable for immediate comparison of the model with…
Dust growth is a crucial step in planet formation, and the efficiency of this process is controlled by the physical and chemical properties of the dust grains. Monte Carlo-based methods are commonly used to follow the collisional evolution…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image…
A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional…
We have modified our particle-by-particle adaptation of the made-to-measure (M2M) method, with the aim of modelling the Galactic disc from upcoming Galactic stellar survey data. In our new particle-by-particle M2M algorithm, PRIMAL, the…
Based on recent advancements in using machine learning for classical density functional theory for systems with one-dimensional, planar inhomogeneities, we propose a machine learning model for application in two dimensions (2D) akin to…
Many fundamental physical processes regarding planetary formation in protoplanetary disks are still imperfectly understood, with an elusive phenomenon being turbulence in such disks. Observations are available of planetary systems and of…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
In modern analysis pipelines, Einstein-Boltzmann Solvers (EBSs) are an invaluable tool for obtaining CMB and matter power spectra. To accelerate the computation of these observables, the CosmicNet strategy is to replace the bottleneck of an…
Stomata play a crucial role in regulating plant physiological processes and reflecting environmental responses. However, accurate and high-throughput stomatal phenotyping remains challenging, as conventional approaches rely on destructive…
While planets are commonly discovered around main-sequence stars, the processes leading to their formation are still far from being understood. Current planet population synthesis models, which aim to describe the planet formation process…
Canonically, a protoplanetary disk is thought to undergo (gravito-)viscous evolution, wherein the angular momentum of the accreting material is transported outwards. However, several lines of reasoning suggest that the turbulent viscosity…
We present the first public release of our generic neural network training algorithm, called SkyNet. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for…
Indoor monocular depth estimation helps home automation, including robot navigation or AR/VR for surrounding perception. Most previous methods primarily experiment with the NYUv2 Dataset and concentrate on the overall performance in their…
We present an algorithm to efficiently sample the full space of planetary interior density profiles. Our approach uses as few assumptions as possible to pursue an agnostic algorithm. The algorithm avoids the common Markov Chain Monte Carlo…
The exoALMA Large Program has revealed a wealth of substructures in the dust and molecular line emission of several protoplanetary discs, suggesting that planet formation may unfold within highly dynamic environments. Using synthetic…
The architectures of exoplanet systems are likely set during the initial planet-formation phase in the circumstellar disk. To understand this process, we have to study the earliest phases of planet formation. Complex sub-structures,…
In our previous study (Tsukamoto {\it et al.} 2023), we investigated formation and early evolution of protoplanetary disks with 3D non-ideal magnetohydrodynamics simulations considering dust growth, and found that the modified equations of…