Results 61 to 70 of about 50,463 (272)
Advanced Intelligent Discovery, EarlyView.This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu +5 more
wiley +1 more source
Enfoque estocástico de la incertidumbre en la selección de carteras de proyectos
Rect@, 2013 In this paper we develop a 0-1 integer multiobjective programming. model to simultaneously select and plan a portfolio of projects from a set of initial proposals. Projects in the portfolio are allowed to start at different moments of time , according to
Pérez García, Fátima
doaj
Advanced Intelligent Discovery, EarlyView.A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
Orthogonality in multiobjective optimization
Applied Mathematics Letters, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alejandro Balbás +2 more
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
The Scientific World Journal, 2014 This paper analyses how different coordination modes and different multiobjective decision making approaches interfere with each other in hierarchical organizations. The investigation is based on an agent-based simulation. We apply a modified NK-model in
Stephan Leitner, Friederike Wall
doaj +1 more source
Dynamic Obstacle Avoidance of Metamorphic Microrobots Using Concentric Sector Navigator
Advanced Intelligent Systems, EarlyView.Metamorphic magnetic microrobots are navigated using a concentric sector navigator for dynamic obstacle avoidance in both microroller and swarm states. After microroller transport, citrate‐triggered dissolution releases nanoparticles that reassemble into a controllable swarm.
Zhaowen Su +4 more
wiley +1 more source
Abstract and Applied Analysis, 2014 A calmness condition for a general multiobjective optimization problem with equilibrium constraints is proposed. Some exact penalization properties for two classes of multiobjective penalty problems are established and shown to be equivalent to the ...
Shengkun Zhu, Shengjie Li
doaj +1 more source
Advanced Intelligent Systems, EarlyView.Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley +1 more source