Numerical analysis of magnetized Ag-Fe<sub>3</sub>O<sub>4</sub>/VPO hybrid nanofluid flow over a spinning inclined disc with Thompson-Troian slip. [PDF]
Ahmad H +5 more
europepmc +1 more source
Efficient Tensor Completion Algorithms for Highly Oscillatory Operators
ABSTRACT We address the problem of recovering highly oscillatory operators, represented as nĂn$$ n\times n $$ matrices with a fixed set of observed entries. Given that these matrices can be well compressed by butterfly matrix decomposition of L=đȘ(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
Quantum algorithms for viscosity solutions to nonlinear Hamilton-Jacobi equations based on an entropy penalization method. [PDF]
Jin S, Liu N.
europepmc +1 more source
ABSTRACT We derive eigenvalue bounds for symmetric blockâtridiagonal multiple saddleâpoint systems preconditioned with blockâdiagonal 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]
Ăetin C +5 more
europepmc +1 more source
OceanForecastBench: A Benchmark Data Set for DataâDriven Global Ocean Forecasting
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, dataâdriven deep learningâbased ocean forecast models, such as XiHe, WenHai, LangYa and AIâGOMS, have demonstrated significant potential in capturing ...
Yi Han +6 more
wiley +1 more source
Abstract PhysicsâInformed Neural Networks (PINNs) have emerged as a powerful framework for modeling groundwater flow using deep learning neural networks, particularly in scenarios where traditional dataâdriven approaches are limited by the scarcity of data.
Adhish Virupaksha +4 more
wiley +1 more source
Hybrid expansion methods for fractional non-linear mathematical systems with Erdelyi-Kober derivative operators in theory of tsunami wave modeling. [PDF]
Damag FH +5 more
europepmc +1 more source
Learning 2D Shallow Water Equations With PhysicsâInformed Neural Operator Networks
Abstract This study investigates the application of PhysicsâInformed Neural Operators (PINOs) for solving the twoâdimensional shallow water equations (2D SWE) in the context of flood modeling. Unlike PhysicsâInformed 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

