Results 141 to 150 of about 4,191 (239)
A Thermodynamic Framework for Turing‐Type Instabilities in Porous Media: Part I Theory
Geochemistry, Geophysics, Geosystems, Volume 27, Issue 3, March 2026.Abstract Pattern formation in geological materials is commonly described using analogies to Turing‐type reaction–diffusion systems, yet a unifying thermodynamic explanation remains elusive. Here we develop a multiscale, thermodynamically consistent framework for pattern‐forming instabilities in porous media undergoing coupled thermo–hydro–mechanical ...
Klaus Regenauer‐Lieb +5 more
wiley +1 more source
Exploring the exact solutions to the nonlinear systems with neural networks method. [PDF]
Sci RepMuhammad J +3 more
europepmc +1 more source
The Arctic Coastal Erosion Model: Overview, Developments, and Calibration at Drew Point, Alaska
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 3, March 2026.Abstract Permafrost coastlines are experiencing significant erosion as polar amplification has enhanced the effects of climate change in the Arctic. Warmer temperatures are increasing thermo‐denudation and more energetic oceans are increasing thermo‐abrasion in unlithified, ice‐bonded permafrost coastlines which comprise at least 40% of the circum ...
Elyce Bayat +10 more
wiley +1 more source
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 3, March 2026.Abstract Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy. We apply four generative diffusion modeling approaches to super‐resolution and inference of forced two‐dimensional quasi‐geostrophic turbulence on the β $\beta $‐plane from coarse, sparse, and gappy observations.
Anantha Narayanan Suresh Babu +2 more
wiley +1 more source
Water Resources Research, Volume 62, Issue 3, March 2026.Abstract Deep learning neural networks (DLNNs) hold great potential for modeling groundwater flow, but their performance depends on data availability. Physics‐informed neural networks (PINNs) help to reduce the reliance of DLNNs on data by integrating physical laws into the training process. This approach is increasingly used in applications related to
Adhish Virupaksha +5 more
wiley +1 more source
NeuberNet: a neural operator solving elastic-plastic partial differential equations at V-notches from low-fidelity elastic simulations. [PDF]
Commun EngGrossi T, Beghini M, Benedetti M.
europepmc +1 more source
Stability for nonlinear diffusive PDEs
, 2019 This thesis focuses on well-posedness and stability estimates (i.e. continuous dependence with respect to the nonlinearities) for a family of cross-diffusion systems that includes several models used to describe cell diffusion in Mathematical Biology for one or more species.
openaire +1 more source
A Deep Learning‐Based Time Shift Objective Function for Full Waveform Inversion
Geophysical Prospecting, Volume 74, Issue 3, March 2026.ABSTRACT Full waveform inversion (FWI) is a powerful technique for estimating high‐resolution subsurface velocity models by minimizing the discrepancy between modelled and observed seismic data. However, the oscillatory nature of seismic waveforms makes point‐wise discrepancy measures highly prone to cycle skipping, especially when the initial velocity
Mustafa Alfarhan +5 more
wiley +1 more source
Nonlinear dynamics of a non-stationary rotor-disk-bearing system with rub-impact and geometric nonlinearity under non-ideal excitation. [PDF]
Sci RepGhasemi MA +2 more
europepmc +1 more source