Results 71 to 80 of about 642 (180)
Reassessing Alpine Permafrost Thermal State by Accounting for Ground Ice
Abstract Permafrost thermal state, representing the stored “cold energy” in the ground, is crucial for assessing permafrost changes. However, the conventional metric, mean annual ground temperature (MAGT), has inherent limitations because it overlooks the thermodynamic contribution of ground ice.
Hailong Ji +4 more
wiley +1 more source
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
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
ABSTRACT The MENA region faces a critical challenge: balancing economic growth spurred by foreign direct investment (FDI) with environmental sustainability. While FDI can bring technological advancements and capital, concerns exist about its potential to exacerbate environmental degradation, particularly carbon emissions.
Brahim Bergougui, Syed Mansoob Murshed
wiley +1 more source
ABSTRACT Aim To characterise the evolution of climatic niches during the diversification of the Phyllotis darwini species group, in order to assess the extent to which divergences involved in radiation were associated with patterns of conservatism or divergence of climatic niches, and whether the differentiation found among climatic niches correlated ...
Marcial Quiroga‐Carmona +4 more
wiley +1 more source
ABSTRACT The accurate prediction of displacement and stress fields in pressure vessels is essential for the safe and reliable design of these structures, particularly when dealing with nonlinear behavior such as that of hyperelastic functionally graded materials (FGMs).
Nasser Firouzi +2 more
wiley +1 more source
A Strictly Geostrophic Product of Sea‐Surface Velocities From the SWOT Fast‐Sampling Phase
Abstract While geostrophy remains the simplest and most practical balance to extract velocity information from sea‐surface height anomaly (SSHa), confusions remain within the oceanographic community to what extent this balance can be applied to altimetric observations with the launch of the Surface Water and Ocean Topography (SWOT) satellite. Given the
Takaya Uchida +6 more
wiley +1 more source
Abstract Mesoscale convective systems (MCSs) are key contributors to heavy rainfall in the East Asian summer monsoon, yet their statistics and internal structure remain difficult to simulate, even in convection‐permitting models (CPMs). This study evaluates the performance of the newly developed Unified Forecast System Double‐Moment microphysics scheme
Taeho Mun +3 more
wiley +1 more source
Collocation method for stochastic delay differential equations
Gergő Fodor +2 more
openaire +1 more source
SPARSE GRID STOCHASTIC COLLOCATION METHOD FOR STOCHASTIC BURGERS EQUATION
Hyung-Chun Lee, Yun Nam
openaire +2 more sources

