Related papers: Learning Neural Operator Surrogates for the Black …
This paper presents a new computer code to solve the general relativistic magnetohydrodynamics (GRMHD) equations using distributed parallel adaptive mesh refinement (AMR). The fluid equations are solved using a finite difference Convex ENO…
Observations of supermassive black holes by the Event Horizon Telescope reveal significant inhomogeneities, most likely related to density and magnetic field perturbations. To model these features, we conduct high-resolution 2D…
The JOREK extended magneto-hydrodynamic (MHD) code is a widely used simulation code for studying the non-linear dynamics of large-scale instabilities in divertor tokamak plasmas. Due to the large scale-separation intrinsic to these…
The numerical study of relativistic magnetohydrodynamics (MHD) plays a crucial role in high-energy astrophysics, but unfortunately is computationally demanding, given the complex physics involved (high Lorentz factor flows, extreme…
We construct the first physics-informed neural-network (PINN) surrogates for relativistic magnetohydrodynamics (RMHD) using a hybrid PDE and data-driven workflow. Instead of training for the conservative form of the equations, we work with…
Many problems at the forefront of theoretical astrophysics require the treatment of magnetized fluids in dynamical, strongly curved spacetimes. Such problems include the origin of gamma-ray bursts, magnetic braking of differential rotation…
Simulating accretion and feedback from the horizon scale of supermassive black holes (SMBHs) out to galactic scales is challenging because of the vast range of scales involved. Elaborating on H. Cho et al., we describe and test a…
We present a detailed three dimensional magnetohydrodynamic (MHD) simulation describing the inner region of a disk accreting onto a black hole. To avoid the technical complications of general relativity, the dynamics are treated in…
It is well established that supermassive black hole (SMBH) feedback is crucial for regulating the evolution of massive, if not all, galaxies. However, modelling the interplay between SMBHs and their host galaxies is challenging due to the…
Iharm3D is an open-source C code for simulating black hole accretion systems in arbitrary stationary spacetimes using ideal general-relativistic magnetohydrodynamics (GRMHD). It is an implementation of the HARM ("High Accuracy Relativistic…
We have written and tested a new general relativistic magnetohydrodynamics (GRMHD) code, capable of evolving MHD fluids in dynamical spacetimes with adaptive-mesh refinement (AMR). Our code solves the Einstein-Maxwell-MHD system of coupled…
This paper proposes a machine-learning-based solution approach for solving multi-horizon stochastic programs. The approach embeds a deep learning neural network into a multi-horizon stochastic program to approximate the recourse operational…
We present a novel spectral solver for general relativistic magnetohydrodynamics on dynamical spacetimes. By combining a high order discontinuous spectral method on mapped Chebyshev Fourier grids, our scheme attains exponential convergence.…
We examine the long-term evolution of accretion tori around black hole (BH) remnants of compact object mergers involving at least one neutron star, to better understand their contribution to kilonovae and the synthesis of r-process…
The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial amounts of high-fidelity data, which can be challenging to…
Higher-order gravitational wave modes from quasi-circular, spinning, non-precessing binary black hole mergers encode key information about these systems' nonlinear dynamics. We model these waveforms using transformer architectures,…
Prevalent around luminous accreting black holes, thin discs are challenging to resolve in numerical simulations. When the disc and black hole angular momentum vectors are misaligned, the challenge becomes extreme, requiring adaptive meshes…
We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive…
Black hole accretion is one of nature's most efficient energy extraction processes. When gas falls in, a significant fraction of its gravitational binding energy is either converted into radiation or flows outwards in the form of black…
Continuum robots enable dexterous manipulation in constrained environments, but require accurate and efficient models for real-time manipulation and control. Traditional physics-based models can be computationally expensive and may suffer…