278 papers
This paper introduces a symmetry-based framework called order-(dis)order family trees that utilizes group-subgroup relations to identify when predicted "novel" ordered crystal structures are actually derived from known disordered phases, thereby providing a critical tool for ensuring genuine novelty in high-throughput materials discovery.
This paper introduces Motion-Enhanced Gravity (MEG), a metric-affine vector–tensor extension of General Relativity that couples spacetime curvature to matter four-momentum flux, offering a mathematically consistent framework that potentially explains flat galactic rotation curves and late-time cosmic acceleration without dark matter while predicting observable deviations in gravitational waves and black hole shadows.
This paper proposes a corrected diffusion equation derived by revisiting Einstein's original method to retain higher-order terms, thereby preserving fluid structure information and enabling accurate modeling of mass transport at nano-scales and in multi-scale systems.
This paper introduces a dual-layer metasurface architecture that utilizes k-space shifting to break intrinsic symmetry and enable compact, single-shot quantitative phase imaging by resolving the polarity of phase gradients for high-fidelity wavefront reconstruction.
This paper presents an analytical investigation of steady magnetohydrodynamic flow and heat transfer in a porous channel with Navier slip and convective boundary conditions, deriving closed-form solutions to demonstrate how parameters such as the magnetic field, slip effects, and heat generation significantly influence velocity profiles, temperature distributions, and key engineering metrics like skin friction and the Nusselt number.
This study proposes a physics-informed neural network framework that accurately models transient elastodynamic wave propagation in bimaterial systems by embedding governing equations and boundary conditions into the learning process, serving as a robust and computationally efficient surrogate model validated against finite-element simulations for applications in high-rate solid mechanics.
This paper proposes "OAM-NET," an AI-driven physical-layer security architecture that utilizes counter-propagating OAM states and a Bi-LSTM neural network to achieve highly resilient data decoding with 92.67% accuracy even under extreme noise conditions where the signal-to-noise ratio is approximately 0 dB.
This study demonstrates that sustained long-length entirely detached crystal growth of Sb-based semiconductors in a Vertical Directional Solidification (VDS) process is achieved through the formation of a self-oriented seed and the subsequent evolution of a dynamic annular gap, resulting in crystals with significantly enhanced structural perfection and reduced dislocation densities.
This study demonstrates that epitaxial, L21-ordered Co2MnSi waveguides with robust structural integrity and intrinsic magnetocrystalline anisotropy enable bias-field-free nonlinear half-metal magnonics by reshaping spin-wave dispersion to suppress instabilities and stabilize low-field operation.
This paper proposes an effective statistical field theory where classical spacetime geometry and gravitational dynamics emerge from the coarse-grained curvature statistics of a confining non-Abelian gauge connection on a dynamical graph, utilizing mechanisms like gauge-to-metric reconstruction and double-copy squaring to derive the metric and graviton while demonstrating through renormalization-group analysis and numerical tests that this framework reproduces Einstein-Hilbert gravity, black hole entropy, and a confinement-deconfinement phase transition.