Related papers: Deep operator neural network applied to efficient …
Cometary activity is a compelling subject of study, with thermophysical models playing a pivotal role in its understanding. However, traditional numerical solutions for small body thermophysical models are computationally intensive, posing…
Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning framework, approximates linear and nonlinear solution…
Accurate models of radiative cooling are a fundamental ingredient of modern cosmological simulations. Without cooling, accreted baryons will not efficiently dissipate their energy and collapse to the centres of haloes to form stars. It is…
The subject of this work is the physical characterization of asteroids, focusing on the thermal inertia of near-Earth asteroids (NEAs). Thermal inertia governs the Yarkovsky effect, a non-gravitational force which significantly alters the…
Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn…
Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many…
Thermal plasma properties play a critical role in plasma simulations and plasma-related applications. However, their strong nonlinear dependence on temperature, pressure, and gas composition makes accurate and efficient evaluation…
Knowledge of the surface thermal inertia of an asteroid can provide insight into surface structure: porous material has a lower thermal inertia than rock. We develop a means to estimate thermal inertia values of asteroids and use it to show…
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that deep neural networks can be used to approximate operators,…
It is now becoming widely accepted that photon recoil forces from the asymmetric reflection and thermal re-radiation of absorbed sunlight are, together with collisions and gravitational forces, primary mechanisms governing the dynamical and…
Fast and accurate predictions for complex physical dynamics are a significant challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in real-world problems. The deep operator…
The thermal inertia of an asteroid's surface can provide insight into regolith properties, such as the presence of a layer of fine dust, the density and thermal conductivity of a rocky surface, and, together with other observational data,…
Thermal inertia determines the temperature distribution over the surface of an asteroid and therefore governs the magnitude the Yarkovsky effect. The latter causes gradual drifting of the orbits of km-sized asteroids and plays an important…
The Yarkovsky effect on real asteroids is complicated to calculate either by analytical or numerical methods, since they are generally irregular in shape. We propose an index to properly characterise the shape of any asteroid, through which…
Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting an increasing attention in many other disciplines, including physical sciences. In…
Field-scale properties of fractured rocks play crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion…
Asteroids smaller than about 100 meters are observed to rotate very fast, with periods often much shorter than the critical limit of 2.2 h. Some of these super-fast rotators can also achieve a very large semi-major axis drift induced by the…
We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations. Due to the appearance of a neural network on the…
Recent discoveries show that asteroids spinning in less than a few minutes undergo sizeable semi-major-axis drifts, possibly driven by the Yarkovsky effect. Analytical formulas can match these drifts only if very low thermal inertia is…
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations, enhancing the generalization of traditional neural…