Results 121 to 130 of about 2,810 (199)

Efficient Tensor Completion Algorithms for Highly Oscillatory Operators

open access: yesNumerical Linear Algebra with Applications, Volume 33, Issue 4, August 2026.
ABSTRACT We address the problem of recovering highly oscillatory operators, represented as n×n$$ n\times n $$ matrices with a fixed set of observed entries. Given that these matrices can be well compressed by butterfly matrix decomposition of L=đ’Ș(logn) levels requiring only O(nlogn)$$ O\left(n\log n\right) $$ degrees of freedom, we propose a novel ...
Navjot Singh   +3 more
wiley   +1 more source

Spectral Analysis of Block Diagonally Preconditioned Multiple Saddle‐Point Matrices With Inexact Schur Complements

open access: yesNumerical Linear Algebra with Applications, Volume 33, Issue 4, August 2026.
ABSTRACT We derive eigenvalue bounds for symmetric block‐tridiagonal multiple saddle‐point systems preconditioned with block‐diagonal Schur complement matrices. This analysis applies to an arbitrary number of blocks and accounts for the case where the Schur complements are approximated, generalizing the findings in [11, Bergamaschi et al., Linear ...
Marco Pilotto   +2 more
wiley   +1 more source

Deterministic, stochastic, and mean-field PDE models in neuroscience. [PDF]

open access: yesFront Comput Neurosci
Çetin C   +5 more
europepmc   +1 more source

OceanForecastBench: A Benchmark Data Set for Data‐Driven Global Ocean Forecasting

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Global ocean forecasting aims to predict key ocean variables such as temperature, salinity, and currents, which is essential for understanding and describing oceanic phenomena. In recent years, data‐driven deep learning‐based ocean forecast models, such as XiHe, WenHai, LangYa and AI‐GOMS, have demonstrated significant potential in capturing ...
Yi Han   +6 more
wiley   +1 more source

Modeling Transient Flow in Heterogeneous Aquifers With the Mixed Pressure‐Velocity Formulation of Physics Informed Neural Networks

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Physics‐Informed Neural Networks (PINNs) have emerged as a powerful framework for modeling groundwater flow using deep learning neural networks, particularly in scenarios where traditional data‐driven approaches are limited by the scarcity of data.
Adhish Virupaksha   +4 more
wiley   +1 more source

Learning 2D Shallow Water Equations With Physics‐Informed Neural Operator Networks

open access: yesWater Resources Research, Volume 62, Issue 8, August 2026.
Abstract This study investigates the application of Physics‐Informed Neural Operators (PINOs) for solving the two‐dimensional shallow water equations (2D SWE) in the context of flood modeling. Unlike Physics‐Informed Neural Networks (PINNs), which require retraining for each new initial or boundary condition (BC), PINOs learn the solution operator ...
Robert Keppler   +2 more
wiley   +1 more source

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