Related papers: GRHayL: a modern, infrastructure-agnostic, extensi…
The gravitational waves emitted by binary neutron star inspirals contain information on nuclear matter above saturation density. However, extracting this information and conducting parameter estimation remains a computationally challenging…
We are on the cusp of detecting gravitational waves (GWs) from individual massive black hole binaries (MBHBs) with the Laser Interferometer Space Antenna and pulsar-timing arrays. These MBHBs may be surrounded by circumbinary disks and…
We study the general-relativistic dynamics of matter being accreted onto and ejected by a magnetised and nonrotating neutron star. The dynamics is followed in the framework of fully general relativistic magnetohydrodynamics (GRMHD) within…
Interest in deploying Deep Neural Network (DNN) inference on edge devices has resulted in an explosion of the number and types of hardware platforms to use. While the high-level programming interface, such as TensorFlow, can be readily…
Merging binaries consisting of two neutron stars (NSs) or an NS and a stellar-mass black hole typically form a massive accretion torus around the remnant black hole or long-lived NS. Outflows from these neutrino-cooled accretion disks…
In this work we present a new hybrid method to simulate the thermal effects of the reionization in cosmological hydrodynamical simulations. The method improves upon the standard approach used in simulations of the intergalactic medium (IGM)…
Numerical relativity is central to the investigation of astrophysical sources in the dynamical and strong-field gravity regime, such as binary black hole and neutron star coalescences. Current challenges set by gravitational-wave and…
We present a highly-scalable framework that targets problems of interest to the numerical relativity and broader astrophysics communities. This framework combines a parallel octree-refined adaptive mesh with a wavelet adaptive…
We introduce our newly developed two different, three dimensional magneto hydrodynamical codes in detail. One of our codes is written in the Newtonian limit (NMHD) and the other is in the fully general relativistic code (GRMHD). Both codes…
Electricity theft, or non-technical loss (NTL), presents a persistent threat to global power systems, driving significant financial deficits and compromising grid stability. Conventional detection methodologies, predominantly reactive and…
Domain scientists increasingly develop Python scripts to analyze satellite imagery but they lack scalability to large-scale data. This paper demonstrates GRAIL, an agentic translation system that converts Python geospatial workflows into…
Many fields of science rely on relational database management systems to analyze, publish and share data. Since RDBMS are originally designed for, and their development directions are primarily driven by, business use cases they often lack…
Perturber objects interacting with supermassive black hole accretion disks are often invoked to explain observed quasi-periodic behavior in active galactic nuclei (AGN). We present global, 3D general relativistic magnetohydrodynamic (GRMHD)…
Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments. This work introduces a novel method based on the Growing…
The direct observation of intermediate-mass black holes (IMBH) populations would not only strengthen the possible evolutionary link between stellar and supermassive black holes, but unveil the details of the pair-instability mechanism and…
We introduce the Generalized Preconditioned Locally Harmonic Residual (GPLHR) method for solving standard and generalized non-Hermitian eigenproblems. The method is particularly useful for computing a subset of eigenvalues, and their eigen-…
Scientific reasoning increasingly requires linking structured experimental data with the unstructured literature that explains it, yet most large language model (LLM) assistants cannot reason jointly across these modalities. We introduce…
While different variants of perceptual losses have been employed in super-resolution literature to synthesize more realistic, appealing, and detailed high-resolution images, most are convolutional neural networks-based, causing information…
Accretion physics has become more important recently due to the detection of the first horizon-scale images of the super-massive black holes of M\,87$^*$ and Sgr~A$^*$ by the Event Horizon Telescope (EHT). General relativistic…
A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input variations. However, current Remote Sensing MLLMs fail to meet this requirement. Trained on…