Related papers: Analysing the SEDs of protoplanetary disks with ma…
We present a novel framework combining Deep Operator Networks (DeepONets) with Physics-Informed Neural Networks (PINNs) to solve partial differential equations (PDEs) and estimate their unknown parameters. By integrating data-driven…
The spectral region around 10 micrometer, showing prominent dust emission bands, is commonly used to derive the chemical composition of protoplanetary dust. Different analysis methods have been proposed for this purpose, but so far, no…
While numerical simulations have been playing a key role in the studies of planet-disk interaction, testing numerical results against observations has been limited so far. With the two directly imaged protoplanets embedded in its…
The spectral energy distributions (SEDs) of galaxies are shaped by nearly every physical property of the system, including the star formation history, metal content, abundance pattern, dust mass, grain size distribution, star-dust geometry,…
To understand the fundamental parameters of galaxy evolution, we investigated the minimum set of parameters that explain the observed galaxy spectra in the local Universe. We identified four latent variables that efficiently represent the…
Protoplanetary disks surrounding young stars are the birth place of planets. Of particular interest are the transition disks with large inner dust cavities of tens of au, hinting at the presence of massive companions. These cavities were…
Context: The global size and spatial distribution of dust is an important ingredient in the structure and evolution of protoplanetary disks and in the formation of larger bodies, such as planetesimals. Aims: We aim to derive simple…
We present a novel method for determining the surface density of protoplanetary disks through consideration of disk 'dust lines' which indicate the observed disk radial scale at different observational wavelengths. This method relies on the…
The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in-situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional…
It is key to constrain the gas surface density distribution, Sigma_gas, as function of disk radius in protoplanetary disks. In this work we investigate if spatially resolved observations of rarer CO isotopologues may be good tracers of…
Circumstellar disks are characteristic for star formation and vanish during the first few Myr of stellar evolution. During this time planets are believed to form in the dense midplane by growth, sedimentation and aggregation of dust.…
Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation…
In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we explore the use of a…
Planet formation models are now often considering the formation of planetary systems, with more than one planet per system. This raises the question of how to represent planetary systems in a convenient way, (e.g. for visualisation purpose)…
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications…
We present a framework for analysing panchromatic and spatially resolved galaxy observations, dubbed SE3D. SE3D simultaneously and self-consistently models a galaxy's spectral energy distribution and its spectral distributions of global…
Machine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials. However, extracting phonon-mode-resolved insights from these…
A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The Discrete Element Method, DEM, was used to generate 92,378 sphere packings that covered many different…
Protoplanetary disks are challenging objects for astrochemical models due to strong density and temperature gradients and due to the UV photons 2D propagation. In this paper, we have studied the importance of several model parameters on the…
Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown the great potential of physics-informed neural networks…