Results 101 to 110 of about 2,370 (192)

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

Water 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, Rasmus Fensholt, Julian Koch   +2 more
wiley   +1 more source

A Hybrid ML‐PDE Framework for Predicting Breaking Ocean Waves

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Wave breaking plays a central role in ocean dynamics, dissipating wave energy and shaping the evolution of the sea surface. Yet, breaking remains difficult to model: envelope‐based models efficiently capture nonlinear wave evolution and are interpretable but exclude breaking, while high‐fidelity direct numerical simulations resolve breaking ...
Y. Liu, T. A. A. Adcock, T. S. van den Bremer, D. Eeltink   +3 more
wiley   +1 more source

Numerical and analytical insights into hybrid nanofluid flow between two stretchable rotating disks using IRPSM and RK4

South African Journal of Chemical Engineering
Background: Rotating disk flows find surprising uses in a variety of disciplines, including engineering processes, computer storage devices, electrical devices, rotating machinery, medical equipment, and many more.
M.M. Seada, Anwar Saeed
doaj   +1 more source

Physics‐Informed Machine Learning Framework to Retroactively Estimate Mantle Thermal Convection From Partial Geophysical Observations

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Mantle convection drives the solid Earth, powering plate motions, volcanism, and earthquakes while regulating planetary heat loss. Reconstructing its history is hampered by sparse, noisy observations concentrated near the surface and the present day. Here I develop an inverse physics‐informed neural network framework to estimate mantle thermal
Atsushi Nakao
wiley   +1 more source

Consequence of Cattaneo-Christov heat and mass flux models on bioconvective flow of dusty hybrid nanofluid over a Riga plate in the presence of gyrotactic microorganisms and Stephan blowing impacts

Case Studies in Thermal Engineering
This study investigates the impact of the Stephan blowing and Cattaneo-Christov flux model on bioconvective flow of dusty hybrid nanofluid over a Riga plate in the presence of gyrotactic microorganisms and variable dust particles volume friction.
Munawar Abbas, Nargis Khan, M.S. Hashmi, Zabidin Salleh, Ayman A. Aly, Shahram Rezapour, Mustafa Inc   +6 more
doaj   +1 more source

A Kolmogorov–Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine learning approaches to determine the most effective data‐driven ...
Leonardo Boledi, Dirk Bosbach, Jenna Poonoosamy   +2 more
wiley   +1 more source

Understanding the Robustness of Reparameterized Full‐Waveform Inversion Through Loss Landscape Perspective: Application to VSP Data

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Full waveform inversion (FWI) reconstructs subsurface models by minimizing the mismatch between observed and simulated seismic data. However, the strong nonlinearity of the inversion problem makes gradient‐based optimization highly sensitive to inaccurate initial models and incomplete observational data.
Ning Wang, Ying Shi, Bingchuan Geng, Xuan Ke   +3 more
wiley   +1 more source

Self‐improving property for certain degenerate functionals with generalized Orlicz growth

Bulletin of the London Mathematical Society, Volume 58, Issue 8, August 2026.
Abstract We investigate a self‐improving property of variational integrals in a weighted framework under generalized Orlicz growth conditions. Assuming that the weight belongs to an appropriate Muckenhoupt class and the growth function satisfies standard structural conditions, we prove that the gradient of any local quasi‐minimizer has local higher ...
Vertti Hietanen, Mikyoung Lee
wiley   +1 more source

Computational Evaluation of Heat and Mass Transfer in Cylindrical Flow of Unsteady Fractional Maxwell Fluid Using Backpropagation Neural Networks and LMS

Mathematics
Fractional calculus plays a pivotal role in modern scientific and engineering disciplines, providing more accurate solutions for complex fluid dynamics phenomena due to its non-locality and inherent memory characteristics.
Waqar Ul Hassan, Khurram Shabbir, Muhammad Imran Khan, Liliana Guran   +3 more
doaj   +1 more source

Kazdan–Warner obstructions for a fourth‐order boundary problem

Bulletin of the London Mathematical Society, Volume 58, Issue 8, August 2026.
Abstract We derive Kazdan–Warner type identities for the boundary problem of prescribing nonconstant interior Q$Q$ curvature and boundary T$T$ curvature on the upper hemisphere S+4${\mathbb {S}}^{4}_{+}$ by a conformal change of the standard metric.
Sergio Cruz‐Blázquez, Azahara DelaTorre   +1 more
wiley   +1 more source