768 papers
This paper demonstrates that transformer-based neural operator surrogates can accurately and efficiently emulate the complex, multiscale dynamics of drift-wave turbulence bifurcation in fusion plasmas, including rare transitions and long-term evolution, thereby offering a computationally viable alternative to direct numerical simulations for real-time control and optimization.
This paper investigates the motion of compressible, inviscid fluid on a rotating sphere by presenting hodograph equations that yield a class of solutions parameterized by two arbitrary functions, providing explicit examples, describing blow-up curves, and analyzing limiting cases of rotation speeds.
This paper establishes a Newtonian-like framework for interacting spiral waves in excitable media, defining a time-varying "mass" and collision-based forces that govern their drift and complex N-body dynamics, with significant implications for understanding cardiac fibrillation.
This study investigates mid-air collisions between free micron-sized droplets and particles to reveal how particle density and wettability govern capture outcomes, leading to a modified effective Weber number that successfully unifies collision regime boundaries across varying size ratios and Ohnesorge numbers.
This paper introduces and validates new explicit fourth-order Multistep Runge-Kutta (MSRK) methods that accelerate Numerical Relativity simulations by reusing data from previous time steps to reduce intermediate stage evaluations, while providing a procedure to tune coefficients for maximizing stable time step sizes.
This study investigates unsteady separated flow over a three-dimensional Gaussian bump at a Reynolds number of $2.26\times10^5$, identifying four distinct broadband phenomena and revealing that the very-low-frequency spanwise meandering of the wake is dynamically coupled with the streamwise stretching of the separation zone.
This paper proposes a supervised conditional flow matching model that effectively synthesizes high-quality, artifact-reduced CT-like images from cone-beam CT scans, significantly improving Hounsfield unit accuracy and image metrics to enable more reliable organ segmentation and dose calculation in image-guided radiotherapy.
This study utilizes a newly developed parallel-kinetic-perpendicular-moment (PKPM) model to simulate the 3D relaxation and reconnection of line-tied flux ropes, revealing a current-dependent transition between diamagnetic and paramagnetic regimes where macroscopic structural differences mask underlying kinetic similarities that are effectively quantified using squashing factor and quasi-potential diagnostics.
This tutorial presents a computationally efficient, two-step Bayesian framework for quantifying uncertainty in linear shock compression data by deriving posterior distributions for model parameters and propagating them through Rankine-Hugoniot equations to generate multiple consistent Hugoniot curves, offering a more robust and interpretable alternative to traditional least squares and bootstrapping methods.
This paper demonstrates that the evolution of political regimes follows universal stochastic principles characterized by critical, non-ergodic dynamics with heavy-tailed step sizes and sojourn times, which can be accurately modeled by a continuous time random walk despite being punctuated by unique historical pathways.
This paper proposes a multiple-echo-enabled harmonic generation (multi-EEHG) scheme that applies successive excitation-echo cycles to a single stored bunch within one revolution, enabling the generation of high-brightness, few-meV coherent X-ray pulses at multiple wavelengths for simultaneous multi-beamline operation in next-generation storage-ring light sources.
This paper proposes a first analytical approach for estimating the viscosity-like parameter of three-phase viscoplastic materials by extending classical averaging equations, bounds, and the Mori-Tanaka method to the three-phase case, with specific fully analytical results provided for dilute inclusions.
This paper presents a novel Bayesian inference framework that integrates molecular dynamics simulations and polarisation analysis to overcome the limitations of conventional fitting methods, successfully resolving the previously ambiguous anisotropic rotational motion of liquid benzene and establishing a new paradigm for understanding molecular dynamics in catalysis and energy materials.
This study employs network theory to analyze a MYH9-focused chemical library, demonstrating that multi-descriptor community detection reveals a robust, consensus-stable core of compounds that can be prioritized for drug repurposing in MYH9-related nephritis.
This study demonstrates that a machine learning-based mesh movement approach (UM2N) integrated into the Thetis software significantly accelerates and robustly enhances the accuracy of non-hydrostatic tsunami simulations for probabilistic coastal hazard assessment.
Using fine-grained individual trajectory data from a major Chinese city, this study constructs a two-dimensional framework to demonstrate that implementing stringent non-pharmaceutical interventions at low activation thresholds achieves optimal epidemic containment with minimal economic cost.
This paper presents a validated experimental and simulation framework that demonstrates the high linearity and precision of a combined LVDT position sensor and voice-coil actuator, confirming its suitability for low-frequency suspension control in gravitational wave detectors.
This paper investigates how the characteristics of laser pulses (long versus ultrashort) shape the initial excited molecular state, demonstrating that the exact factorization framework challenges standard Born-Huang concepts like population transfer and vertical excitation by revealing a more complex dependence on the light source.
This paper utilizes a novel Full-F gyrofluid model to simulate 2D Harris-sheet magnetic reconnection in low-beta plasmas, demonstrating that the non-normal nature of the evolution operator enables significant transient amplification that drives explosive reconnection and explains the transition from linear tearing to rapid nonlinear acceleration.
This paper presents a large-scale, actor-based model integrating detailed Dutch demographic and mobility data to simulate novel respiratory pathogen transmission on dynamic contact networks, demonstrating how geographic and demographic factors drive epidemic spread and quantifying the impact of interventions like self-isolation and travel restrictions.