Results 61 to 70 of about 1,315 (192)

On the impact of observation‐error correlations in data assimilation, with application to along‐track altimeter data

Quarterly Journal of the Royal Meteorological Society, EarlyView.
We study the impact of observation‐error correlations in data assimilation using both a simple idealised system and a more realistic configuration. A spectral analysis of data assimilation in the idealised system allows us to gain insights on the effect of observation‐error correlations, which are then validated using the realistic configuration.
Olivier Goux, Anthony T. Weaver, Selime Gürol, Oliver Guillet, Andrea Piacentini   +4 more
wiley   +1 more source

Quantum Iterative Methods for Solving Differential Equations with Application to Computational Fluid Dynamics

Advanced Quantum Technologies, EarlyView.
Quantum algorithms for differential equations are developed with applications in computational fluid dynamics. The methods follow an iterative simulation framework, implementing Jacobi and Gauss–Seidel schemes on quantum registers through linear combinations of unitaries.
Chelsea A. Williams, Antonio A. Gentile, Vincent E. Elfving, Daniel Berger, Oleksandr Kyriienko   +4 more
wiley   +1 more source

Discrete orthogonal polynomials on Gauss–Lobatto Chebyshev nodes

Journal of Approximation Theory, 2007
AbstractIn this paper, we present explicit formulas for discrete orthogonal polynomials over the so-called Gauss–Lobatto Chebyshev points. In particular, this allows us to compute the coefficient in the three-terms recurrence relation and the explicit formulas for the discrete inner product.
Eisinberg A, FEDELE, Giuseppe
openaire   +3 more sources

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

A Non‐Intrusive Efficient Two‐Step Model Reduction Method for Large‐Scale Thin‐Walled Acoustic Indirect BEM Systems

International Journal for Numerical Methods in Engineering, Volume 126, Issue 20, 30 October 2025.
ABSTRACT Performing frequency sweep analysis for acoustic performance prediction using the boundary element method (BEM) is associated with high computational costs as the associated system matrices are dense and frequency‐dependent. While model order reduction (MOR) techniques, such as Krylov subspace recycling, have been proposed for direct BEM ...
Philip Le, Dionysios Panagiotopoulos, Elke Deckers   +2 more
wiley   +1 more source

Assessing dosimetric benefit from daily online adaptive radiation therapy for esophageal cancer

Journal of Applied Clinical Medical Physics, Volume 26, Issue 10, October 2025.
Abstract A cohort of 19 esophageal cancer patients treated at our institution were analyzed to assess the clinical feasibility and dosimetric benefit from daily adaptive radiation therapy (ART). An Ethos‐planning template was developed to generate the initial ethos reference plan and daily adaptive plans using 9‐field IMRT for reduced on‐treatment ...
Antonia Kubiatowicz, Michael Sherer, Ayaka Owaga, Andrew Sharabi, Xenia Ray   +4 more
wiley   +1 more source

Multi‐Objective Reinforcement Learning for Automated Resilient Cyber Defence

Applied AI Letters, Volume 6, Issue 3, October 2025.
Autonomous Cyber Defence agents were trained using multi‐objective reinforcement learning to defend a computer network against red‐agent attack, trading off network security and service availability. A Pareto Front was established (lower left), which shows the trade off between accessing network services (green) and defending against compromised ...
Ross O'Driscoll, Claudia Hagen, Joe Bater, James Adams   +3 more
wiley   +1 more source

Dynamics of Bio‐Convection Two‐Phase Nanofluid Flow in an Entropy‐Optimized Non‐Newtonian Model With Nonlinear Thermal Radiation and Electromagnetic Effects

Energy Science &Engineering, Volume 13, Issue 10, Page 4955-4972, October 2025.
ABSTRACT The study of nanofluids has attracted significant attention due to their superior thermophysical properties, making them ideal for thermal transport in engineering and biomedical applications. Motivated by these capabilities, this study develops a novel three‐dimensional mathematical model for electrically conducting Sutterby nanofluids ...
A. M. Obalalu, Umair Khan, Muhammad Waqas, Nermeen Abdullah, Samia Elattar   +4 more
wiley   +1 more source

Bernstein polynomials method for solving multi-order fractional neutral pantograph equations with error and stability analysis

Results in Applied Mathematics
In this investigation, we present a new method for addressing fractional neutral pantograph problems, utilizing the Bernstein polynomials method.
M.H.T. Alshbool
doaj   +1 more source

Rician Likelihood Loss for Quantitative MRI With Self‐Supervised Deep Learning

NMR in Biomedicine, Volume 38, Issue 10, October 2025.
We introduce a numerically accurate and stable negative log Rician (NLR) likelihood loss for quantitative MR imaging with self‐supervised deep learning. Self‐supervised neural networks trained with the NLR loss have reduced bias in intra‐voxel incoherent motion diffusion coefficient at low signal‐to‐noise ratio (SNR) compared to the traditional mean ...
Christopher S. Parker, Anna Schroder, Sean C. Epstein, James Cole, Daniel C. Alexander, Hui Zhang   +5 more
wiley   +1 more source