742 papers
This paper presents a fully-coupled simulation demonstrating that high-voltage pulsed electric fields can effectively mitigate reentry communication blackout by depleting electron density in the plasma sheath, thereby reducing signal attenuation from 60% to 4% with a lightweight, feasible power system, while revealing that ion kinetics primarily govern the sheath's topology.
This paper presents an exact analytical solution for the two-dimensional Ising model in an external magnetic field by employing a modified Clifford algebraic approach that incorporates topological structures and a Yang-Baxter-determined Lorentz-like rotation to derive the partition function and characterize the system's first-order magnetization process.
This paper demonstrates that a reinforcement learning controller, trained on a data-driven reduced-order model (DManD) combining POD, autoencoders, and neural ODEs, successfully stabilizes high-Rayleigh-number Rayleigh-Bénard convection and reduces heat transfer by 16–23% when deployed in direct numerical simulations.
This paper presents the design, test results, and Monte-Carlo simulations of a new heavy-ion detector utilizing a 10-picosecond resolution RF Timer to achieve precise background suppression and separation of prompt and delayed events for direct measurements of heavy hypernuclei lifetimes.
This paper introduces the SHallow REcurrent Decoder (SHRED), a novel deep learning architecture that successfully reconstructs full-state subsurface turbulent flow fields from sparse surface height measurements or video footage, enabling robust inference up to two integral length scales deep using as few as three sensors.
This paper presents a first-principles simulation method that incorporates both droplet and bath deformation to accurately predict non-coalescing droplet rebounds at low Weber numbers, a capability validated by new experimental data.
Direct numerical simulations of plane Couette-Poiseuille flow near the transition to turbulence reveal that streak waviness acts as a quadratic function of roll amplitude when the latter is sufficiently large, thereby validating a critical component of Waleffe's self-sustaining process model.
This paper investigates shear-driven wave generation in a two-fluid system with an exponential velocity profile, revealing a novel transition from Kelvin-Helmholtz to Holmboe and finally to Miles critical layer instabilities as the density ratio decreases, a finding validated by both linear theory and nonlinear simulations that demonstrate the first unified observation of all three canonical instabilities within a single background state.
This paper investigates third-order deep-water wave-induced drift using a reduced Hamiltonian formulation, revealing that while classical Stokes drift slightly misestimates particle motion, incorporating difference harmonic terms significantly improves agreement with nonlinear trajectory calculations across various sea states.
This paper presents an exact Eulerian framework for computing the topological entropy of stationary three-dimensional turbulence using only local strain-rate eigenvalue distributions and decorrelation times obtainable from a single fixed probe, thereby eliminating the need for challenging Lagrangian particle tracking.
This paper presents a unifying Bayesian framework for sparse-view plasma tomography that integrates data likelihood and profile priors into a posterior distribution, enabling efficient uncertainty quantification and principled statistical analysis through a stochastic gradient flow algorithm validated on TCV tokamak soft x-ray data.
The paper introduces a new "Pairs and Squares" rendering of the Periodic Table that leverages the fact that the number of orbitals at each atomic energy level forms a perfect square to create a more regular and intuitive structure compared to existing presentations.
This study demonstrates that the complex rupture dynamics of the 2011 Tohoku-Oki megathrust earthquake, including multiple rupture episodes and large tsunamigenic slip, can be self-consistently explained by the interplay of preexisting fault heterogeneity and rapid coseismic frictional restrengthening.
By applying model selection and dynamical systems theory to body temperature data from a hibernating Arctic ground squirrel, researchers identified a low-dimensional, environmentally sensitive control architecture that not only explains interbout arousals but also qualitatively predicts temperature modulation patterns across diverse species including birds, shrews, and bears.
This paper introduces a novel methodology for analyzing neutron and x-ray scattering event data that bypasses traditional histogramming and least-squares fitting to achieve significantly higher accuracy and efficiency while reducing systematic errors, despite requiring increased computation time and offering a less intuitive approach.
The paper introduces EarthquakeNPP, a rigorous benchmark for earthquake forecasting that reveals current Neural Point Process models fail to outperform the classical ETAS model, highlighting the need for improved collaboration between seismology and machine learning communities.
This paper applies Prigogine's thermodynamics of open systems to formulate a law for individual organism growth and development, demonstrating through experimental comparison that specific entropy decreases across the evolutionary sequence from yeast to birds.
This paper proposes a Federated Learning framework for LEO 6G Non-Terrestrial Networks that leverages High-Altitude Platform Stations to distribute beam selection tasks, demonstrating that a Graph Neural Network model outperforms a Multi-Layer Perceptron in prediction accuracy and stability, especially at low elevation angles.
This study employs pump-probe numerical simulations to investigate the light-induced nonadiabatic photodissociation of the NaH molecule, revealing how the interplay between multiple electronic conical intersections, electron-rotation coupling, and rotational motion governs ultrafast dissociation probabilities, kinetic energy release, and fragment angular distributions.
This paper introduces "Pufflets" as inertial counterparts to Stokeslets to demonstrate that metachronal waves of cilia can drive highly efficient particle transport through inertial coasting, a mechanism impossible in low-Reynolds-number Stokes flow.