Related papers: Deep operator neural network applied to efficient …
We present an adaptation of the ATPM to simultaneously predict the Yarkovsky and YORP effects in the presence of global self-heating that occurs within the large concavities of irregularly shaped asteroids, which has been neglected or…
Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…
The field of asteroid thermophysical modeling has experienced an extraordinary growth in the last ten years, as new thermal infrared data became available for hundreds of thousands of asteroids. The infrared emission of asteroids depends on…
Crystal plasticity (CP) simulations are a tool for understanding how microstructure morphology and texture affect mechanical properties and are an essential component of elucidating the structure-property relations. However, it can be…
The near-Earth objects (NEOs) (3200) Phaethon and (155140) 2005 UD are thought to share a common origin, with the former exhibiting dust activity at perihelion that is thought to directly supply the Geminid meteor stream. Both of these…
Traditionally, 1D models based on scaling laws have been used to parameterized convective heat transfer rocks in the interior of terrestrial planets like Earth, Mars, Mercury and Venus to tackle the computational bottleneck of high-fidelity…
Modeling plays a critical role in additive manufacturing (AM), enabling a deeper understanding of underlying processes. Parametric solutions for such models are of great importance, enabling the optimization of production processes and…
Shape optimization is essential in aerospace vehicle design, including reentry systems, and propulsion system components, as it directly influences aerodynamic efficiency, structural integrity, and overall mission success. Rapid and…
While it is widely known that neural networks are universal approximators of continuous functions, a less known and perhaps more powerful result is that a neural network with a single hidden layer can approximate accurately any nonlinear…
In this paper, we introduce a physics and geometry informed neural operator network with application to the forward simulation of acoustic scattering. The development of geometry informed deep learning models capable of learning a solution…
A direct and local deep learning (DL) model for atomic forces is presented. We demonstrate the model performance in bulk aluminum, sodium, and silicon; and show that its errors are comparable to those found in state-of-the-art machine…
Precise soft landings on asteroids are central to many deep space missions for surface exploration and resource exploitation. To improve the autonomy and intelligence of landing control, a real-time optimal control approach is proposed…
Optical lightcurves can be used for the shape and spin reconstruction of asteroids. Due to unknown albedo, these models are scale-free. When thermal infrared data are available, they can be used for scaling the shape models and for deriving…
Thermophysical Models (TPMs), which have proven to be a powerful tool in the interpretation of the infrared emission of asteroid surfaces, typically make use of a priori obtained shape models and spin axes for use as input boundary…
This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The…
Ground settlement prediction during the process of mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing process-oriented…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
By means of a varied-shape thermophysical model (VS-TPM) of Hanus et al. (2015) that takes into account asteroid shape and pole uncertainties, we analyze the thermal IR data acquired by the NASA's WISE satellite of about 300 asteroids with…
The surface temperature of an asteroid is fundamental information for the design of an exploration mission and the interpretation of scientific observations. In addition, the thermal radiation of the asteroid causes a non-gravitational…
Temperature profiles of the hot galaxy cluster intracluster medium (ICM) have a complex non-linear structure that traditional parametric modelling may fail to fully approximate. For this study, we made use of neural networks, for the first…