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Aims. The main goal of the present paper is to provide the first systematic numerical study of the propagation of astrophysical relativistic jets, in the context of high-resolution shock-capturing resistive relativistic magnetohydrodynamics…
We present a unified framework to efficiently approximate solutions to fractional diffusion problems of stationary and parabolic type. After discretization, we can take the point of view that the solution is obtained by a matrix-vector…
This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent…
We study linear and nonlinear development of relativistic and ultrarelativistic current sheets of pair plasmas with antiparallel magnetic fields. Two types of two-dimensional problems are investigated by particle-in-cell simulations. First,…
In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote RL both in research and real-world applications, frameworks are…
Document parsing, as a fundamental yet crucial vision task, is being revolutionized by vision-language models (VLMs). However, the autoregressive (AR) decoding inherent to VLMs creates a significant bottleneck, severely limiting parsing…
This paper introduces the Random-Key Optimizer (RKO), a versatile and efficient stochastic local search method tailored for combinatorial optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys…
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning regards selecting appropriate keywords included into the input,…
Numerical relativity is an essential tool for solving Einstein's equations of general relativity for dynamical systems characterized by high velocities and strong gravitational fields. The implementation of new algorithms that can solve…
We develop a numerical solver for radiative transfer problems based on the weighted essentially nonoscillatory (WENO) scheme modified with anti-diffusive flux corrections, in order to solve the temperature and ionization profiles around a…
Nuclear fission reactors are abundant sources of antineutrinos. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond…
We present a collection of tools automating the efficient computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however,…
We developed and provide AnalyticLC, a novel analytic method and code implementation for dynamical modeling of planetary systems, including non-coplanar interactions, based on a disturbing function expansion to fourth order in…
Chain-of-thought (CoT) enhances the problem-solving ability of large language models (LLMs) but incurs substantial inference cost due to long autoregressive trajectories. Existing acceleration strategies either shorten traces via early…
Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches…
The Fokker-Planck equation models rare events across sciences, but its high-dimensional nature challenges classical computers. Quantum algorithms for such non-unitary dynamics often suffer from exponential {decay in} success probability. We…
Through detailed numerical simulations, we demonstrate that relativistic outflows (Lorentz factor $\Gamma \sim 7$) of electron-positron pairs can be produced by radiative acceleration even when the flow starts from a nearly pair equilibrium…
Thermochemistry, ray-tracing radiation, and radiation-matter interactions are important processes which are computationally difficult to model in astrophysical simulations, addressed by introducing novel algorithms optimized for…
This paper describes a multidimensional hydrodynamic code which can be used for the studies of relativistic astrophysical flows. The code solves the special relativistic hydrodynamic equations as a hyperbolic system of conservation laws…
In high-energy astronomical phenomena, the stochastic particle acceleration by turbulences is one of the promising processes to generate non-thermal particles. In this paper, we investigate the energy-diffusion efficiency of relativistic…