786 papers
This paper derives first-principles hydrodynamic forces for spherical droplets in unsteady Stokes flows to demonstrate that neglecting the Basset-Boussinesq history force leads to significant errors, specifically overestimating droplet deflection amplitudes by more than 60% in horizontally shaken liquids, particularly for light particles and bubbles.
This paper demonstrates that increasing activity or reducing instability timescales in two-dimensional dense active suspensions drives a structural transition to a mixed state of locally polar-ordered regions and chaotic domains, characterized by intense vortices, giant number fluctuations, and universal statistical behavior unified by an energy-based order parameter.
This paper introduces "spotlight sampling," an approximate sampling scheme that leverages fragmented Hamiltonians and correlated sampling to reduce the computational cost of predicting energy differences in Variational Monte Carlo from standard scaling to essentially linear with system size, as demonstrated in tests on bond stretching and -system delocalization.
This review paper aims to bridge the gap between advanced mathematical concepts like chaos, cantori, and turnstiles and their practical applications in toroidal plasma physics by demonstrating how these frameworks can resolve longstanding misunderstandings and advance solutions for critical issues such as magnetic reconnection, stellarator divertors, and runaway electron disruptions.
This paper presents a reduced-cost, state-specific frozen natural orbital-based third-order algebraic diagrammatic construction method for electron attachment that achieves significant computational speedups and high accuracy, including for challenging non-valence anions, by employing density fitting, truncated natural auxiliary functions, and perturbative corrections.
The paper introduces -PIC, a Python-controlled framework designed to facilitate the development, cross-testing, and comparison of next-generation particle-in-cell solvers and extensions that overcome traditional limitations like conserved quantity preservation and unbiased down-sampling.
This paper introduces the Spin-MInt algorithm, the first rigorously symplectic and time-reversible propagator designed to directly simulate nonadiabatic dynamics using spin-mapping variables, demonstrating superior accuracy and computational efficiency compared to existing methods across various models.
This study demonstrates that dielectric-barrier discharge plasma actuators mounted on the A-pillars of a heavy-duty truck model effectively reduce aerodynamic drag by shrinking lateral separation bubbles, with leeward-side actuation proving most effective at mitigating drag and side forces under crosswind conditions.
This paper presents an extended kinetic theory model for incompressible turbulent flows that improves consistency with established turbulence theory, enables accurate simulation of wall-bounded flows through low-Reynolds number damping, and successfully captures non-Newtonian effects by demonstrating that averaged turbulent flows behave similarly to rarefied gas flows.
This paper demonstrates that global geometric design of a wave field can directly control the motion of a localized, self-propelled walking droplet, enabling topological phenomena such as band-gap exclusion, edge-guided transport, and chirality-dependent orbital dynamics.
This paper generalizes a thermodynamically consistent vibrational-electron heating model to include multi-quantum overtone transitions, thereby correcting significant systematic heating errors in high-energy regimes and ensuring accurate electron temperature predictions for non-equilibrium plasma applications.
This paper introduces a deterministic Boltzmann equation solver for proton and heavy ion beam therapy that eliminates the random noise inherent in Monte Carlo methods while preserving their high-fidelity physics, thereby enabling fast and accurate dose and fluence spectrum calculations for treatment planning.
The paper introduces Flo, a data-driven graph neural network model built on the Anemoi framework that simulates North Sea storm surges at 4 km resolution with accuracy comparable to traditional numerical models, marking a significant step toward integrating machine learning into storm surge forecasting.
This paper systematically reviews and analyzes the correspondence between discrete linear lattice models and their continuous partial differential equation counterparts across various boundary conditions, utilizing Fourier analysis to examine their relationship primarily through dispersion relations.
This paper introduces a reproducible hybrid quantum-classical framework that combines Gaussian kernel-based quantum-inspired feature mapping with normalized structural descriptors in a Deep Quantum Neural Network to achieve superior cross-context generalization and robustness in predicting residue-level pKa values compared to classical models.
The paper introduces Matlantis-PFP v8, a universal machine learning interatomic potential trained on the more accurate r2SCAN functional rather than PBE, which achieves systematically improved agreement with experimental data and high-accuracy references across diverse chemical domains without requiring domain-specific fine-tuning.
This paper resolves the well-known pathologies of the Lorentz Abraham Dirac equation by modeling charged particles as finite, deformable spheres with internal breathing modes, thereby deriving a causal radiation reaction force that eliminates pre-acceleration and runaway solutions while providing a mechanical interpretation of the Schott term as reversible internal energy storage.
This paper presents two novel microfluidic platforms—one utilizing pillar arrays and the other employing a custom Pt-coated PMMA photomask—for the in situ micropatterning of PEGDA-PEG hydrogels to enable precise control and tracking of molecular diffusion for diverse applications ranging from biosensing to drug delivery.
This paper presents a novel high-order gas-kinetic scheme integrated with the actuator line model and immersed boundary method on GPUs to accurately simulate three-dimensional wind turbine flows, including nacelle and tower effects, while validating its ability to capture complex wake interactions and turbulent statistics against experimental data.
By applying a deep learning workflow to seismic data from the 2025 Santorini-Amorgos crisis, researchers expanded the earthquake catalogue from 4,000 to 80,000 events, revealing unprecedented fluid-driven volcanic-tectonic swarms and identifying a new deep magmatic reservoir beneath Anydros Islet.