Results 81 to 90 of about 1,373 (161)

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

Journal 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, Haoming Jia, Xiang Wang, Huizan Wang, Wei Wu, Jianming Zheng, Peikun Xiao   +6 more
wiley   +1 more source

Smoothness and stability in the Alt-Phillips problem. [PDF]

Math Ann
Carducci M, Tortone G.
europepmc   +1 more source

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

An optimal Petrov-Galerkin framework for operator networks. [PDF]

Comput Methods Appl Mech Eng
Charles P, Ray D, Yu Y, Prins J, Melchers H, Abdelmalik MRA, Cochran J, Oberai AA, Hughes TJR, Larson MG.   +9 more
europepmc   +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

PINN for stiff moving-boundary PDE to predict the locking point in superheated steam drying. [PDF]

Sci Rep
Malekjani N, Bück A, Tsotsas E, Kharaghani A.   +3 more
europepmc   +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

Poroelasticity derived from the microstructure for intrinsically incompressible constituents. [PDF]

Z Angew Math Phys
Penta R, Lonati C, Miller L, Marzocchi A.   +3 more
europepmc   +1 more source

Optimization Algorithm for Determining the Source Surface Radius Based on Parker Solar Probe In Situ Measurements From Encounters 1 to 19

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The Potential Field Source Surface (PFSS) extrapolation is a method for estimating the large scale coronal magnetic field from photospheric magnetograms. The source surface serves as the outer boundary of its solution domain, and is typically a spherical surface.
Shiouhe Wang, Fang Shen, Yi Yang, Xueshang Feng, Jiansen He   +4 more
wiley   +1 more source

An Inverse Signorini Obstacle Problem. [PDF]

Arch Ration Mech Anal
de Hoop MV, Lassas M, Lu J, Oksanen L, Zhao Z.   +4 more
europepmc   +1 more source