Related papers: Learning Neural Operator Surrogates for the Black …
A new general relativistic magnetohydrodynamics (GRMHD) code ``RAISHIN'' used to simulate jet generation by rotating and non-rotating black holes with a geometrically thin Keplarian accretion disk finds that the jet develops a spine-sheath…
We present 3D general relativistic magnetohydrodynamic(GRMHD) simulations of zero angular momentum accretion around a rapidly rotating black hole, modified by the presence of initially uniform magnetic fields. We consider serveral angles…
Highly precise and robust waveform models are required as improvements in detector sensitivity enable us to test general relativity with more precision than ever before. In this work, we introduce a spin-aligned surrogate ringdown model.…
Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates promise evaluation speedups, but their accuracy relies on…
Numerical effects are known to plague adaptive mesh refinement (AMR) codes when treating massive particles, e.g. representing massive black holes (MBHs). In an evolving background, they can experience strong, spurious perturbations and then…
General relativistic magnetohydrodynamic (GRMHD) simulations represent a fundamental tool to probe various underlying mechanisms at play during binary neutron star (BNS) and neutron star (NS) - black hole (BH) mergers. Contemporary…
Gravitational-wave detectors have begun to observe coalescences of heavy black holes at a consistent pace for the past few years. Accurate models of gravitational waveforms are essential for unbiased and precise estimation of source…
We propose Super-resolution Neural Operator (SRNO), a deep operator learning framework that can resolve high-resolution (HR) images at arbitrary scales from the low-resolution (LR) counterparts. Treating the LR-HR image pairs as continuous…
Accretion of magnetized gas on compact astrophysical objects such as black holes has been successfully modeled using general relativistic magnetohydrodynamic (GRMHD) simulations. These simulations have largely been performed in the Kerr…
Astrophysical plasmas in relativistic spacetimes, such as black hole accretion flows, are often weakly collisional and require kinetic modeling to capture non-local transport and particle acceleration. However, the extreme scale separation…
We develop a comprehensive mathematical and computational framework for sequential surrogate modeling of three-phase black-oil reservoir dynamics using neural operators, with particular emphasis on Fourier Neural Operators (FNO) and their…
Numerical relativity (NR) simulations of binary black hole (BBH) systems provide the most accurate gravitational wave predictions, but at a high computational cost -- especially when the black holes have nearly extremal spins (i.e. spins…
(Abridged) We here continue our effort to model the behaviour of matter when orbiting or accreting onto a generic black hole by developing a new numerical code employing advanced techniques geared solve the equations of in…
We present two general relativistic radiation magnetohydrodynamics (GRRMHD) simulations of magnetically arrested disks (MADs) around non-spinning ($a_*=0$) and spinning ($a_*=0.9$) supermassive black holes (BHs). In each simulation, the…
Simulating a binary black hole (BBH) coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate…
We present global 3D GRMHD simulations of black hole (BH) accretion disks designed to investigate how MRI-driven dynamo action regulates jet formation and evolution. Unlike standard SANE/MAD setups that impose a coherent large-scale…
We present the first astrophysical application of the ECHO code in its recent version supplemented by a generalized Ohm law, namely a kinematic study of dynamo effects in thick accretion disks. High-order \emph{implicit-explicit}…
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without costly numerical relativity (NR) simulations, but generally…
Pulsar timing arrays (PTAs) can detect the low-frequency stochastic gravitational-wave background (GWB) generated by an ensemble of supermassive black hole binaries (BHBs). Accurate determination of BHB merger timescales is essential for…
Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries, among other applications. We face the challenge of modeling…