Results 121 to 130 of about 543 (203)

A Variational Physics‐Informed Neural Network Framework for Simulating Nonlinear Elastic Wave Propagation

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.
ABSTRACT This work employs Variational Physics‐Informed Neural Networks (VPINNs) to solve the forward problem of one‐dimensional wave propagation in nonlinear elastic solids. A first‐order elastodynamic formulation is adopted, where particle velocity and strain serve as the primary field variables.
Benjamin Ankay, Okyay Altay
wiley   +1 more source

Complex soliton wave patterns of Gross-Pitaevskii systems: application in quantum and optical engineering. [PDF]

Sci Rep
Bilal M, Alawaideh YM, Rehman SU, Popa IL, Al-Khamiseh BM, Ghannam H.   +5 more
europepmc   +1 more source

Reducing Parametric Problems With Discontinuities Using Deep Learning

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.
ABSTRACT Model order reduction becomes necessary when dealing with large‐scale engineering problems governed by parametric Partial Differential Equations (PDEs). Conventional projection based reduced basis methods, e.g., based on proper orthogonal decomposition, are not only intrusive in nature but may also demand tailored strategies to efficiently ...
Viral Chheda, Margarita Chasapi
wiley   +1 more source

A numerical study of mean corrected phase-averaging in geophysical flow equations. [PDF]

Theor Comput Fluid Dyn
Andrews TC, Wingate BA.
europepmc   +1 more source

Symplecticity‐Preserving Prediction of Parameter‐Dependent Hamiltonian Dynamics by Generalized Kernel Interpolation

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.
ABSTRACT We extend the kernel‐based symplectic predictor introduced by Herkert et al. (2026) to a parameter‐augmented setting in which the learned flow‐map surrogate depends not only on the state, but also on additional variables such as physical parameters and macro time‐step sizes. The method uses a product kernel ansatz on a parameter and macro step
Robin Herkert, Tobias Ehring, Bernard Haasdonk   +2 more
wiley   +1 more source

Extended Divergence-Measure Fields, the Gauss-Green Formula and Cauchy Fluxes. [PDF]

Arch Ration Mech Anal
Chen GG, Irving C, Torres M.
europepmc   +1 more source

Four Decades of Research at the MADE Site and Beyond: Advancing the Understanding and Modeling of Solute Transport in Heterogeneous Media

Reviews of Geophysics, Volume 64, Issue 4, December 2026.
Abstract Spatial heterogeneity, manifested as strong variability in hydraulic conductivity (K) within geological formations, exerts a fundamental control on groundwater flow and solute transport. Because K can vary over multiple orders of magnitude in complex three‐dimensional patterns, predicting contaminant plume migration remains a long‐standing ...
Zhilin Guo, Maosheng Yin, Peiyao Dong, Yong Zhang, Kewei Chen, Graham Fogg, Brian Berkowitz, Marco Dentz, Chunmiao Zheng   +8 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

Data‐Driven Discovery of Classical and Anomalous Transport Models From Sparse Breakthrough Curves in Heterogeneous Porous Media via Symbolic Regression

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 6, December 2026.
Abstract Natural porous media are highly heterogeneous, with flow and transport controlled by pore‐space morphology and connectivity. Consequently, classical continuum models often fail to describe observed transport under strong heterogeneity. A key challenge is identifying physically interpretable transport models from sparse observational data.
Jinwoo Im, Felipe P. J. de Barros, Sami Masri, Muhammad Sahimi   +3 more
wiley   +1 more source

Moving from table to graph in physics-informed spatio-temporal symbolic regression. [PDF]

Sci Rep
Lazebnik T, Liberzon A.
europepmc   +1 more source