Related papers: Modeling Dense Stellar Systems: Background
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and…
A huge amount of good quality astrophysical data converges towards the picture of a spatially flat universe undergoing the today observed phase of accelerated expansion. This new observational trend is commonly addressed as Precision…
Modeling galaxy formation in a cosmological context presents one of the greatest challenges in astrophysics today, due to the vast range of scales and numerous physical processes involved. Here we review the current status of models that…
This volume contains the proceedings of MARS 2015, the first workshop on Models for Formal Analysis of Real Systems, held on November 23, 2015 in Suva, Fiji, as an affiliated workshop of LPAR 2015, the 20th International Conference on Logic…
Discrete element modelling (DEM) is one of the most efficient computational approaches to the fracture processes of heterogeneous materials on mesoscopic scales. From the dynamics of single crack propagation through the statistics of crack…
More than twenty years ago, it was predicted that nuclei can adopt interesting shapes, such as rods or slabs, etc., in the cores of supernovae and the crusts of neutron stars. These non-spherical nuclei are referred to as nuclear "pasta".…
We present Starduster, a supervised deep learning model that predicts the multi-wavelength SED from galaxy geometry parameters and star formation history by emulating dust radiative transfer simulations. The model is comprised of three…
We study the dynamical evolution of idealised stellar systems by averaging results from many $N$-body simulations, each having modest numbers of stars. For isolated systems with stars of uniform mass, we discuss aspects of evolution up to…
The stability of rotating isotropic spherical stellar systems is investigated by using N-body simulations. Four spherical models with realistic density profiles are studied: one of them fits the luminosity profile of globular clusters,…
I present an overview of the hierarchy of structures existing in the interstellar medium (ISM) and the possible mechanisms that cause the fragmentation of one level into the next, with the formation of stars as its last step. Within this…
While 3D foundational models have shown promise for promptable segmentation of medical volumes, their robustness to imprecise prompts remains under-explored. In this work, we aim to address this gap by systematically studying the effect of…
We summarize here the discussions around photospheric constraints, current uncertainties in models of stellar atmospheres, and reports on ongoing spectroscopic surveys. Rather than a panorama of the state of the art, we chose to present a…
In this chapter we review recent advances in understanding the roles that magnetic fields play throughout the star formation process, gained through observations and simulations of molecular clouds, the dense, star-forming phase of the…
The evolution of massive stars is the basis of several astrophysical investigations, from predicting gravitational-wave event rates to studying star-formation and stellar populations in clusters. However, uncertainties in massive star…
This volume contains the joint proceedings of IMPEX 2017, the first workshop on Handling IMPlicit and EXplicit knowledge in formal system development and FM&MDD, the second workshop on Formal and Model-Driven Techniques for Developing…
Massive stars like company. Here, we provide a brief overview of progresses made over the last 5 years by a number of medium and large surveys. These results provide new insights on the observed and intrinsic multiplicity properties of main…
Models which allow an explicit application to structurally modulated substances are reviewed within the frame of a symmetry-based approach starting from discrete lattice theory. Focus is set on models formulated in terms of local variables…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…
Transfer learning has recently become the dominant paradigm of machine learning. Pre-trained models fine-tuned for downstream tasks achieve better performance with fewer labelled examples. Nonetheless, it remains unclear how to develop…
This volume contains the proceedings of MARS 2017, the second workshop on Models for Formal Analysis of Real Systems, held on April 29, 2017 in Uppala, Sweden, as an affiliated workshop of ETAPS 2017, the European Joint Conferences on…