Results 151 to 160 of about 11,954,654 (221)

A Model Intercomparison Study of Mixed‐Phase Clouds in a Laboratory Chamber

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 9, September 2026.
Abstract Supercooled liquid water often persists in mixed‐phase clouds for hours to days, even though a simple, well‐mixed Wegener‐Bergeron‐Findeisen model predicts rapid depletion of liquid water in the presence of ice. The persistence is well documented, but the relative importance of the maintaining mechanisms remains uncertain.
Aaron Wang, Sisi Chen, Steven K. Krueger, Piotr Dziekan, Kotaro Enokido, Fabian Hoffmann, Agnieszka Makulska, Bernhard Mehlig, Gaetano Sardina, Grigory Sarnitsky, Silvio Schmalfuß, Shin‐ichiro Shima, Fan Yang, Mikhail Ovchinnikov, Raymond A. Shaw   +14 more
wiley   +1 more source

NORi: An ML‐Augmented Ocean Boundary Layer Parameterization

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 9, September 2026.
Abstract NORi is a machine learning (ML) parameterization of ocean boundary layer (BL) turbulence that is physics‐based and augmented with neural networks. NORi stands for neural ordinary differential equations Richardson number (Ri) closure. The physical parameterization is controlled by a Richardson number‐dependent diffusivity and viscosity.
Xin Kai Lee, Ali Ramadhan, Andre Souza, Gregory LeClaire Wagner, Simone Silvestri, John Marshall, Raffaele Ferrari   +6 more
wiley   +1 more source

Irreversible Stirring and Mixing of Tracers by Oceanic Internal Gravity Waves

AGU Advances, Volume 7, Issue 6, September 2026.
Abstract Oceanic internal waves are often thought to be ineffective in stirring and mixing tracer fields such as heat, salt, carbon, and other nutrients. Due to their oscillatory nature, waves are expected to merely displace tracers periodically around their mean position.
C. P. Sanjay, Jim Thomas
wiley   +1 more source

A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models. [PDF]

PLoS One
Mannan A, Rahman JU, Alzahrani E, Fiidow OA.   +3 more
europepmc   +1 more source

Interval Data Assimilation: A Data Assimilation Scheme Where All Observations Are Intervals

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 9, September 2026.
Abstract With the proliferation of non‐conventional observations from low‐cost sensors, citizen science networks, and binary detection systems, data assimilation increasingly confronts observations that take the form of inequalities or intervals rather than precise point values.
Le Duc, Naoki Hiura, Yohei Sawada
wiley   +1 more source

Stochastic modeling of pneumonia transmission dynamics and implications for public health control. [PDF]

Front Public Health
Raza A, Lampart M, Sumelka W, Rocha EM, Alruwaili F, Fadhal E.   +5 more
europepmc   +1 more source

Simple Harmonic Motion Analysis of Elastic Structures Using Hamiltonian Simulation via Quantum Singular Value Transformation

International Journal for Numerical Methods in Engineering, Volume 127, Issue 16, 30 August 2026.
ABSTRACT This study proposes a quantum computing algorithm for dynamic structural analysis, assuming the use of quantum computers. Specifically, we apply a quantum algorithm known as Hamiltonian Simulation (HS), which calculates the time evolution of the Schrödinger equation with time‐independent coefficients, to the equations of motion governing the ...
Koya Wagatsuma, Katsuhiro Endo, Kenjiro Terada   +2 more
wiley   +1 more source

Solving Differential Equations in R: Package deSolve [PDF]

In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved ...
Karline Soetaert, Thomas Petzoldt, R. Woodrow Setzer   +2 more
core  

FPGA realization of fractional-order chaotic oscillators using ABM and EFORK methods. [PDF]

Sci Rep
Pérez JCN, Prieto RUA, Afolabi OM, Pinal FJP, Valdez MAE.   +4 more
europepmc   +1 more source

Vertical Wind‐Pressure Covariance and its Vertical Gradient in Atmospheric Boundary Layers From Large Eddy Simulations

Geophysical Research Letters, Volume 53, Issue 16, 28 August 2026.
Abstract Atmospheric wind‐pressure covariances (e.g., vertical wind‐pressure covariance, w′p′‾ $\overline{{w}^{\mathit{\prime }}{p}^{\mathit{\prime }}}$) affect boundary‐layer energetics and pressure‐related corrections, yet their sign and vertical structure are inconsistently reported.
Yanzhao Zhou, Heping Liu, Xin Li
wiley   +1 more source