Related papers: The Pulsar Magnetosphere with Machine Learning: Me…
Magnetospheric twists, that is magnetospheres with a toroidal component, are under scrutiny due to the key role the twist is believed to play in the behaviour of neutron stars. Notably, its dissipation is believed to power magnetar…
We propose that the magnetosphere reconfiguration induced by magnetic twists in the closed field line region can account for the mode-switching of intermittent pulsars. We carefully investigate the properties of axisymmetric force-free…
We present a novel numerical method that allows the calculation of nonlinear force-free magnetostatic solutions above a boundary surface on which only the distribution of the normal magnetic field component is given. The method relies on…
Soon after the discovery of radio pulsars in 1967, the pulsars are identified as strongly magnetic (typically $10^{12}$G) rapidly rotating ($\sim 10^{2}-0.1$ Hz) neutron stars. However, the mechanism of particle acceleration in the pulsar…
Pulsar magnetospheres are filled with relativistic pairs copiously emitting photons detected from the radio wavelengths up to high and very high energies, in the GeV and sometimes in the TeV range. Efficient particle acceleration converts…
NICER has observed a few millisecond pulsars where the geometry of the X-ray emitting hotspots on the neutron star is analyzed in order to constrain the mass and radius from X-ray light curve modeling. One example, PSR J0030+0451, is shown…
I survey recent successes in the application of relativistic MHD and force-free electrodynamics to the modeling of the pulsars' rotational energy loss mechanism as well as to the structure and emission characteristics of Pulsar Wind…
The aim of this paper is to introduce a FieldTNN-based machine learning method for solving the Maxwell eigenvalue problem in both 2D and 3D domains, including both tensor and non-tensor computational regions. First, we extend the existing…
We have explored the magnetosphere described by a numerical solution for an axisymmetric rotating field satisfying the force-free and ideal MHD conditions everywhere. The electric current distribution is determined by the requirement of…
We present a novel approach to compute three-dimensional Magnetohydrodynamic equilibria by parametrizing Fourier modes with artificial neural networks and compare it to equilibria computed by conventional solvers. The full nonlinear global…
In this paper, we develop a novel mesh-free framework, termed physics-informed neural networks with invariant measure score matching (PINN-IMSM), for reconstructing dynamical systems from unlabeled point-cloud data that capture the system's…
We present an application of Physics-Informed Neural Networks to handle MultiPhase-Field simulations of microstructure evolution. It has been showcased that a combination of optimization techniques extended and adapted from the PINNs…
A technique is proposed for constructing three-dimensional solutions comlplying with the self-consistent magnetohydrostatic (MHS) equations and with observations along the line of sight of the magnetic field at the photosphere. The…
We describe a new scheme for evolving the equations of force-free electrodynamics, the vanishing-inertia limit of magnetohydrodynamics. This pseudospectral code uses global orthogonal basis function expansions to take accurate spatial…
A common scientific inverse problem is the placement of magnets that produce a desired magnetic field inside a prescribed volume. This is a key component of stellarator design, and recently permanent magnets have been proposed as a…
A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics.…
Magnetospheres of pulsars are thought to be filled with plasma, and variations in plasma supply can affect both pulsar emission properties and spin-down rates. A number of recently discovered "intermittent" pulsars switch between two…
We apply physics-informed neural networks (PINNs) to first-order two-scale periodic asymptotic homogenization of the property tensor in a generic elliptic equation. The problem of lack of differentiability of property tensors at the sharp…
Numerical computation of the ideal Magnetohydrodynamic (MHD) equilibrium magnetic field is at the base of stellarator optimisation and provides the starting point for solving more sophisticated Partial Differential Equations (PDEs) like…
Physics-informed neural networks (PINNs) have emerged as a promising numerical method based on deep learning for modeling boundary value problems, showcasing promising results in various fields. In this work, we use PINNs to discretize…