Related papers: Neutron star envelopes with machine learning: a si…
With the development of ever-improving telescopes capable of observing exoplanet atmospheres in greater detail and number, there is a growing demand for enhanced 3D climate models to support and help interpret observational data from space…
We present a comprehensive analysis of the effects of models with very low reheating scenarios ($T_\text{RH} \sim \mathcal{O}(\text{MeV})$) on the cosmological observables and derive corresponding bounds on the reheating temperature. With…
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…
The synthesis of hyper-heavy elements is investigated under conditions simulating neutron star environment. The Constrained Molecular Dynamics (CoMD) approach is used to simulate low energy collisions of extremely n-rich nuclei. A new type…
Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potential for machine learning applications and offer a unifying view of common tensor decomposition models…
The prediction reliability of neural networks is important in many applications. Specifically, in safety-critical domains, such as cancer prediction or autonomous driving, a reliable confidence of model's prediction is critical for the…
In numerical simulations of core-collapse supernova and binary neutron stars mergers, information about the energetics and composition of matter is implemented via external tables covering the huge ranges of thermodynamic conditions…
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for…
We use deep sparsely connected neural networks to measure the complexity of a function class in $L^2(\mathbb R^d)$ by restricting connectivity and memory requirement for storing the neural networks. We also introduce representation system -…
Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable…
We propose Trusted Neural Network (TNN) models, which are deep neural network models that satisfy safety constraints critical to the application domain. We investigate different mechanisms for incorporating rule-based knowledge in the form…
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and…
Neutron stars are astrophysical laboratories of many extremes of physics. Their rich phenomenology provides insights into the state and composition of matter at densities which cannot be reached in terrestrial experiments. Since the core of…
Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase…
Neutron star mergers are amongst the most promising sources for the joint detection of gravitational waves and electromagnetic signals. They are also potential sites for the production of r-process elements and probes of the equation of…
This paper examines the coincidence of neural networks with numerical methods for solving spatiotemporal physical problems. Neural networks are used to learn predictive numerical models from trajectory datasets from two well understood 1D…
The implications of hyperon pairing for the thermal evolution of neutron stars containing hyperons are investigated. The outcome of cooling simulations are compared for neutron star models composed only of nucleons and leptons, models…
We assess the impact of accurate, self-consistent modelling of thermal effects in neutron-star merger remnants in the context of third-generation gravitational-wave detectors. This is done through the usage, in Bayesian model selection…
We study the systematics of neutron star cooling curves with three representative masses from the most populated interval of the estimated mass distribution for compact objects. The cooling simulations are made in the framework of the…
Exploring the equation of state of dense matter is an essential part of interpreting the observable properties of neutron stars. We present here the first results for dense matter in the zero-temperature limit generated by the MUSES…