Results 121 to 130 of about 1,563 (194)
Respirology, Volume 31, Issue 6, Page 561-577, June 2026.ABSTRACT Chronic obstructive pulmonary disease (COPD) remains the third leading cause of death worldwide, and conventional bronchodilator‐based therapies have limited efficacy in preventing exacerbations and disease progression. The 2024–2026 period represents a historic inflection point: three mechanistically distinct agents received Food and Drug ...
Naoya Fujino, Hisatoshi Sugiura
wiley +1 more source
Small Structures, Volume 7, Issue 6, June 2026.Multiscale modeling of battery systems combines quantum‐mechanical calculations, atomistic simulations, and mesoscale phase‐field approaches to describe processes spanning from reaction energetics to morphology evolution. Establishing consistent links between these scales remains a key challenge, particularly for the transfer of physical descriptors ...
Shoutong Jin +3 more
wiley +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract Simulations of numerical weather prediction models indicate that the atmosphere possesses an intrinsic limit of predictability. Initial perturbations of tiny amplitude grow quickly in areas of convection and latent heat release, then spread out and move upscale, eventually affecting even the largest planetary scales after about 2 weeks.
T. Selz, G. C. Craig
wiley +1 more source
Pricing Options under Heston’s Stochastic Volatility Model via Accelerated Explicit Finite Differencing Methods [PDF]
We present an acceleration technique, effective for explicit finite difference schemes describing diffusive processes with nearly symmetric operators, called Super-Time-Stepping (STS).
Conall O'Sullivan, Stephen O'Sullivan
core
Information Flow in Geophysical Systems
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 6, June 2026.Abstract We present a new framework for analyzing the evolution of information in geophysical systems. Understanding how information, and its counterpart, uncertainty, propagates is central to predictability studies and has significant implications for applications such as forecast uncertainty quantification and risk management. It also offers valuable
P. J. van Leeuwen
wiley +1 more source
Cohomogeneity‐one solitons in Laplacian flow: Local, smoothly‐closing and steady solitons
Journal of the London Mathematical Society, Volume 113, Issue 6, June 2026.Abstract We initiate a systematic study of cohomogeneity‐one solitons in Bryant's Laplacian flow of closed G2$\text{G}_2$‐structures on a 7‐manifold, motivated by the problem of understanding finite‐time singularities of that flow. Here, we focus on solitons with symmetry groups Sp(2)${\rm Sp}(2)$ and SU(3)${\rm SU}(3)$; in both cases, we prove the ...
Mark Haskins, Johannes Nordström
wiley +1 more source
On a diffuse interface approach to PDEs on surfaces and networks
, 2019 Diffuse interface models based on the phase field methodology have been developed for various free boundary problems. They involve representing the interfaces by thin layers.
Stinner, Bjoern
core
Stable factorization of the Calderón problem via the Born approximation
Journal of the London Mathematical Society, Volume 113, Issue 6, June 2026.Abstract In this article, we prove the existence of the Born approximation in the context of the radial Calderón problem for Schrödinger operators. The Born approximation naturally appears as the linear component of a factorization of the Calderón problem; we show that the nonlinear part, obtaining the potential from the Born approximation, enjoys ...
Thierry Daudé +3 more
wiley +1 more source
Analytic and numerical solution of free boundary fluid flow through porous media
Free boundary problems (FBP) arise in many applications, including in the flow of porous media. They involve the solution of a partial differential equation (PDE) that has an unknown boundary. The unknown boundary is called a free boundary, and it should
Faisal Muteb K. Almalki (21286943)
core
Implementing physics-informed neural networks with deep learning for differential equations. [PDF]
Front Artif IntellEmmert-Streib F +3 more
europepmc +1 more source