Results 131 to 140 of about 11,022,643 (271)

Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors

AIChE Journal, Volume 72, Issue 10, October 2026.
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley   +1 more source

On the accuracy of Runge-Kutta’s method [PDF]

Mathematics of Computation, 1951
openaire   +1 more source

Edge‐Based Discretizations on Triangulations in Any Dimension, With Special Attention to Four‐Dimensional Space

International Journal for Numerical Methods in Fluids, Volume 98, Issue 10, Page 1143-1164, October 2026.
This article provides important geometric formulas for node‐centered, edge‐based schemes in any number of dimensions. These formulas are noteworthy, as they do not require the explicit formation of dual regions. We prove several key geometric results, with a particular focus on the four‐dimensional case, due to potential space‐time applications ...
Nicholas Tufillaro, David M. Williams, Hiroaki Nishikawa   +2 more
wiley   +1 more source

A Comparative Analysis of Numerical Methods for Mathematical Modelling of Intravascular Drug Concentrations Using a Two-Compartment Pharmacokinetic Model

Mathematical and Computational Applications
Pharmacokinetic modelling is extensively used in understanding drug behavior, distribution and optimizing dosing regimens. This study presents a two-compartment pharmacokinetic model developed using three numerical approaches that includes the Euler ...
Kaniz Fatima, Basit Ali, Abdul Attayyab Khan, Sadique Ahmed, Abdelhamied Ashraf Ateya, Naveed Ahmad   +5 more
doaj   +1 more source

Subspace Acceleration for Efficient Nonlinear Water Wave Simulation

International Journal for Numerical Methods in Fluids, Volume 98, Issue 10, Page 1165-1180, October 2026.
We introduce an exponentially weighted subspace acceleration technique to reduce GMRES iterations for solving the Poisson equation with time‐dependent coefficients in nonlinear, dispersive free‐surface flows governed by the incompressible Navier‐Stokes equations. The method significantly reduces memory requirements and computational complexity compared
Rasmus Kleist Hørlyck Sørensen, Margherita Guido, Allan Peter Engsig‐Karup, Daniel Kressner   +3 more
wiley   +1 more source

QG‐Python: A Reproducible 1.5‐Layer Quasi‐Geostrophic Modelling Service With Direct Sparse Solvers, Spectral Stability Diagnostics and Filter Sensitivity Data

Geoscience Data Journal, Volume 13, Issue 4, October 2026.
QG‐Python is an open, reproducible 1.5‐layer quasi‐geostrophic modelling service whose published diagnostics show that the classical von Neumann bound underestimates the true Leapfrog stability limit by a factor of 13.7, and that the Robert–Asselin–Williams filter reduces kinetic energy and enstrophy biases from ~21% and ~45% to under 4%.
Elias D. Nino‐Ruiz
wiley   +1 more source

Multi‐Objective Bayesian Co‐Optimization of Parameterized Moving Horizon Estimation and Model Predictive Control

International Journal of Robust and Nonlinear Control, Volume 36, Issue 15, Page 7193-7213, October 2026.
ABSTRACT This paper proposes a Machine Learning (ML)‐enabled estimator‐controller design framework, in which a parameterized Model Predictive Controller (MPC) and a parameterized Moving Horizon Estimator (MHE) are jointly refined using Bayesian Optimization (BO).
Hossein Nejatbakhsh Esfahani, Javad Mohammadpour Velni   +1 more
wiley   +1 more source

Examination of bio convection with nanoparticles containing microorganisms under the influence of magnetism fields on vertical sheets by five-order Runge-Kutta method.

Heliyon, 2023
Fathollahi R, Alizadeh A, Safari Y, Nabi H, Shamsborhan M, Taghinia F.   +5 more
europepmc   +1 more source

How Much Physics Should a Neural Network Know? Thermodynamics‐Informed Neural Networks for Rock Constitutive Modeling With Epistemic Uncertainty

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Machine learning offers a flexible route to constitutive modeling, with two emergent questions for geoscience applications: what level of thermodynamic constraint should be embedded in the network architecture, and how should predictive uncertainty be quantified when extrapolating from laboratory to field conditions? We address both challenges
Kangan Li, Tushar Mittal, Benjamin K. Holtzman, Seth Saltiel   +3 more
wiley   +1 more source

A Stochastic Model for Fog Forecasting

Geophysical Research Letters, Volume 53, Issue 17, 16 September 2026.
Abstract Despite significant advancements in parameterizations of boundary layer processes, forecasting, and nowcasting low‐level clouds using numerical models remain challenging. The purpose of this study is to test a prototype of a high‐resolution stochastic‐deterministic model designed to simulate the life cycle of fog cover based on the Ising model
E. Cardoso‐Bihlo, B. Khouider
wiley   +1 more source