Results 121 to 130 of about 1,840,782 (294)

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Globally convergent modifications of particle swarm optimization for unconstrained optimization

, 2011
We focus on the solution of a class of unconstrained optimization problems, wherethe evaluation of the objective function is possibly costly and the use of exact algorithmsmay require a too large computational burden.
Fasano Giovanni, Peri Daniele, Campana Emilio Fortunato   +2 more
core  

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Stability analysis tool for tuning unconstrained decentralized model predicitive controllers [PDF]

, 2006
Some processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often infeasible in dealing with large scale plants and they are technologically prohibitive when the processes are too fast for the ...
Vaccarini, M., Katebi, M.R., Longhi, S.
core   +2 more sources

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

Sufficient descent directions in unconstrained optimization

Unconstrained optimization, Sufficient descent direction, PSB method, Global convergence, Superlinear convergence,
Dong-Hui Li, Yunhai Xiao, Xiao-Min An
core   +1 more source

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

Advanced Intelligent Discovery, EarlyView.
Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung, Seongmin Kim, Jaehyun Park, Chaeyoung Park, Zhihao Xu, Sanghyo Hwang, Baiqing Ye, Youn sang Kim, Sun‐Kyung Kim, Tengfei Luo, Eungkyu Lee   +10 more
wiley   +1 more source

Multi-Objective Optimization Technique Based on QUBO and an Ising Machine

IEEE Access
With an increase in the complexity of society, solving multi-objective optimization problems (MOPs) has become crucial. In this study, we introduced a novel method called “quadratic unconstrained binary optimization based on the weighted normal ...
Hiroshi Ikeda, Takashi Yamazaki
doaj   +1 more source

Reconfigurable Design and Optimization‐Based Control of an Integrated Leg‐Arm Robot for Space Applications With an Underactuated Body

Advanced Intelligent Systems, EarlyView.
A quadrupedal integrated leg‐arm robot with an underactuated reconfigurable body is developed. By using a Sarrus mechanism as the body, the robot enables reconfiguration through its supporting limbs, achieving mode switching without additional actuators.
Xinghan Zhuang, Kun Xu, Shutao Wu, Haoyuan Liu, Bochao Song, Jiawei Chen, Peijin Zi, Tao Zhang, Xilun Ding   +8 more
wiley   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source