790 papers
This study demonstrates that while both linearised and nonlinear methods accurately recover mean models in Bayesian Full Waveform Inversion, nonlinear approaches are essential for obtaining accurate uncertainty estimates and unbiased meta-property calculations, as linearised methods fail to capture complex uncertainty structures due to the inherent nonlinearity of wave physics.
This paper proposes a self-consistent, numerically efficient simulation model based on a scattered-field formulation that couples geometric wakefield and space charge effects to accurately assess their non-negligible impact on beam quality in high-brilliance electron sources like the SuperKEK photo-gun.
This paper presents the design of a buildable, optimized stellarator for the EPOS experiment, demonstrating that key metrics for electron-positron plasma confinement and engineering feasibility can be achieved through advanced optimization tools and the evaluation of eight candidate configurations.
Using anonymized mobile phone data from Bogotá, this study reveals that while pandemic restrictions reduced commuting across all income groups, lower-income populations rebounded faster to pre-pandemic levels than higher-income groups, a disparity driven by territorial characteristics and digital connectivity inequalities.
This study presents the first systematic investigation of individual water droplet dynamics in airtanker firefighting, revealing that release height and relative humidity are the dominant factors governing evaporation and transport, with only droplets between 150 μm and 3 mm in radius successfully reaching the ground.
This paper presents a novel, computationally efficient real-time Bayesian technique for estimating radiated power from specific plasma regions at the TCV tokamak using pre-optimized linear combinations of bolometer data, enabling its integration into plasma control systems without relying on prior synthetic training or tomographic reconstruction.
This paper proposes a compact radio-frequency photoelectron multiplier tube with sub-10 ps temporal resolution, designed based on experimental measurements of photoelectron radial spreading and SIMION simulations, for use in medical time-correlated single-photon counting applications.
This study demonstrates that the spin-opposite variant of one-body Møller-Plesset perturbation theory (O2BMP2) offers a computationally efficient and highly accurate method for predicting inverted singlet-triplet gaps in INVEST molecules, achieving benchmark-level precision comparable to high-cost methods like ADC(3) and EOM-CCSD while enabling high-throughput screening for OLED applications.
This paper proposes a multi-branch shell model with a hierarchically organized geometry that successfully reproduces the correct thermal spectra and numerically demonstrates the emergence of the dual energy-enstrophy cascades characteristic of two-dimensional turbulence, overcoming a key limitation of classical shell models.
This paper demonstrates that the recently developed one-body Møller–Plesset perturbation theory (OBMP2), when applied within a SCF framework, provides a robust and highly accurate method for predicting K-edge excitation energies in both closed- and open-shell systems, outperforming established techniques like DFT and EOM-CCSD.
This paper outlines the current configuration, capabilities, and recent technical advancements of the Tokamak à Configuration Variable's infrared thermography systems while highlighting that parasitic infrared light and surface layer heat transmission factors remain the primary sources of uncertainty in heat flux measurements.
This paper reports the first successful in-vivo human magnetic particle imaging (MPI) angiography, demonstrating the technique's ability to visualize real-time venous perfusion in the upper extremity using a human-scale scanner and clinically approved nanoparticles, thereby establishing MPI as a radiation-free, clinically translatable alternative to X-ray angiography.
This paper introduces a physics-augmented machine learning framework that utilizes thermodynamic descriptors derived from molecular dynamics simulations to enable accurate and extrapolable prediction of normal boiling points for diverse chemical classes, including inorganic compounds and salts, where traditional structure-based models fail.
This paper introduces a kinetic multi-agent model coupling SIS epidemic dynamics with opinion-driven compliance, demonstrating how asymmetric feedback from infection events and failed transmissions shapes collective behavior through a rigorously derived macroscopic limit that captures the interplay between disease prevalence and opinion polarization.
This paper demonstrates that the feasibility of the "15-minute city" paradigm is fundamentally constrained by the internal structure of urban economies and firm-size distributions, revealing that universal short commutes are mathematically impossible in highly concentrated employment centers without significant economic restructuring or differentiated mobility strategies.
This paper presents a rigorous theoretical derivation of the one-body double-hybrid density functional (OBDHF) theory, which resolves the self-consistency issues of conventional double-hybrid functionals by embedding OBMP2 correlation directly into the generalized Kohn-Sham effective Hamiltonian to enable fully self-consistent calculations without requiring optimized effective potentials.
This paper presents a new optimization method within the OOPS framework that combines poloidal omnigenity and piecewise omnigenity to generate stellarator configurations with favorable neoclassical transport and bootstrap current properties while relaxing strict omnigenity constraints.
This paper derives and numerically verifies exact scaling laws for isotropic binary fluid turbulence, extending classical Kolmogorov results to the Cahn-Hilliard-Navier-Stokes framework by incorporating both bulk flow and interfacial dynamics.
This study analyzes interviews with six upper-division physics majors to reveal that while they view generative AI as a useful learning resource, they remain skeptical of its accuracy and primarily conceptualize their own creativity within the "mini-c" and "little-c" frameworks of the Four C Model.
This study utilizes the framework of Physics Computational Literacy to analyze semi-structured interviews with upper-division students, revealing that they navigate tradeoffs in developing various literacy aspects and approach social computational literacy differently based on unspoken assumptions.