Results 81 to 90 of about 2,991 (173)
International Transactions in Operational Research, Volume 33, Issue 4, Page 2499-2534, July 2026.Abstract This paper presents a two‐stage model for planning a renewable energy portfolio by balancing economic, social and environmental sustainability goals. The first stage addresses a multi‐objective problem where conflictive impacts generated by the energy portfolios should be optimised according to the corresponding economic, social or ...
Amelia Bilbao‐Terol +2 more
wiley +1 more source
Advanced Science, Volume 13, Issue 33, 15 June 2026.We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich +2 more
wiley +1 more source
Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Fourier Mass Lower Bounds for Batchelor‐Regime Passive Scalars
Communications on Pure and Applied Mathematics, Volume 79, Issue 6, Page 1449-1466, June 2026.ABSTRACT Batchelor predicted that a passive scalar ψν$\psi ^\nu$ with diffusivity ν$\nu$, advected by a smooth fluid velocity, should typically have Fourier mass distributed as |ψ̂ν|2(k)≈|k|−d$|\widehat{\psi }^\nu |^2(k) \approx |k|^{-d}$ for |k|≪ν−1/2$|k| \ll \nu ^{-1/2}$.
William Cooperman, Keefer Rowan
wiley +1 more source
Engineering Reports, Volume 8, Issue 6, June 2026.The study introduces a model‐free data‐driven control strategy combining sliding mode control with a projection recurrent neural network to regulate HIV dynamics. The method eliminates dependence on mathematical models, ensures constrained optimal drug dosing, and robustly drives HIV states to a healthy equilibrium despite uncertainty. ABSTRACT In this
Ashkan Zarghami +2 more
wiley +1 more source
Engineering Reports, Volume 8, Issue 6, June 2026.Non‐Newtonian blood flow through multiple tilted ellipsoidal stenoses is numerically investigated using the DeKee‐Turcotte‐Papanastasiou model. The results reveal asymmetric velocity fields, elevated wall shear stress, significant pressure drops, and shear‐dependent thermal effects, highlighting the critical hemodynamic risks associated with eccentric ...
Azad Hussain, Huma Naz
wiley +1 more source
Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials
International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 8, Page 3331-3354, 10 June 2026.ABSTRACT Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the
Farinaz Mostajeran, Salah A. Faroughi
wiley +1 more source
Discrete and Continuous Models and Applied Computational ScienceEarlier we developed a stable fast numerical algorithm for solving ordinary differential equations of the first order. The method based on the Chebyshev collocation allows solving both initial value problems and problems with a fixed condition at an ...
Konstantin P. Lovetskiy +3 more
doaj +1 more source
Personalized Differential Privacy for Ridge Regression Under Output Perturbation
Naval Research Logistics (NRL), Volume 73, Issue 4, Page 525-537, June 2026.ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya +3 more
wiley +1 more source
Optimal Control‐Based Generic Framework for Radiofrequency Pulse Design in MRI
NMR in Biomedicine, Volume 39, Issue 6, June 2026.This paper presents an open‐source Python‐based optimal control RF design framework, which can tackle various problems (short‐T2 selective excitation or B1‐robust excitation/inversion). It features three main methodological contributions: a specific cost is introduced to reduce pulse peak amplitude; consistent integration of various hard constraints on
Emilio Molina +2 more
wiley +1 more source