Results 21 to 30 of about 435 (145)
Generative Models for Crystalline Materials
Advanced Materials, Volume 38, Issue 18, 25 March 2026.Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni +15 more
wiley +1 more source
Advanced Science, Volume 13, Issue 14, 9 March 2026.We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu +7 more
wiley +1 more source
Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis
Statistics in Medicine, Volume 45, Issue 3-5, February 2026.ABSTRACT The presence of intermediate confounders, also called recanting witnesses, is a fundamental challenge to the investigation of causal mechanisms in mediation analysis, preventing the identification of natural path‐specific effects. Common alternatives (such as randomizational interventional effects) are problematic because they can take non ...
Tat‐Thang Vo +4 more
wiley +1 more source
IET Control Theory &Applications, Volume 20, Issue 1, January/December 2026.Maintaining reliable tracking control in networked control systems over fading wireless channels is difficult due to the stochastic and time‐correlated nature of wireless links. This work proposes a transition‐aware Q‐learning (TA‐QL) method that learns robust control policies directly from networked data without requiring explicit models, while ...
Ehsan Badfar, Babak Tavassoli
wiley +1 more source
Varying confidence levels for CVaR risk measures and minimax limits [PDF]
, 2018 Conditional value at risk (CVaR) has been widely studied as a risk measure. In this paper we add to this work by focusing on the choice of confidence level and its impact on optimization problems with CVaR appearing in the objective and also the ...
Xu, Huifu, Anderson, Edward, Zhang, Dali
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Digital Twins and Applications, Volume 3, Issue 1, January/December 2026.ABSTRACT To enhance the techno‐economic performance and robustness of multi‐microgrids (MMG) systems, this paper proposes a two‐stage bi‐level collaborative optimisation strategy integrating energy sharing and price incentives. In the day‐ahead stage, the shared energy storage operator (SESO) at the upper level employs conditional Wasserstein ...
Xianghu Cui +4 more
wiley +1 more source
Electronics Letters, Volume 62, Issue 1, January/December 2026.This paper introduces a heterogeneous graph attention‐enhanced deep reinforcement learning‐based scheduling framework designed to instantaneously generate high‐quality production plans for production scheduling in cloud manufacturing. The proposed framework has been tested on multiple public datasets and industrial instances, and compared against ...
Shiduo Ning +5 more
wiley +1 more source
IET Smart Grid, Volume 9, Issue 1, January/December 2026.To support the ‘Dual Carbon’ strategy, this paper proposes a bilevel optimisation model for distributed energy storage configuration considering wind‐solar uncertainty and carbon trading. Using SNGAN and K‐means for scenario generation, the model minimises total cost at the upper level and grid vulnerability/power losses at the lower level.
Xiaonan Li +4 more
wiley +1 more source
IET Smart Grid, Volume 9, Issue 1, January/December 2026.This paper proposes a two‐phase risk‐averse optimisation strategy of pre‐scheduling‐rolling dispatch for considering the grid integration of cluster wind power during cold wave. This method aims to address the imbalance between supply and demand in the power system that may be caused by the low output power of cluster wind power and load surges ...
Jiankang Zhang +4 more
wiley +1 more source
An Interior-Point algorithm for Nonlinear Minimax Problems [PDF]
We present a primal-dual interior-point method for constrained nonlinear, discrete minimax problems where the objective functions and constraints are not necessarily convex.
E. Obasanjo, G. Tzallas-Regas, B. Rustem
core