Results 131 to 140 of about 5,147,506 (303)
Advanced Intelligent Discovery, EarlyView.This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo +3 more
wiley +1 more source
Pareto set for the Adult9a dataset.
The Pareto set is composed of 17 elements selected out of 68 possible candidate solutions.
Hatem A. Khater (18297656) +2 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.With the downstream filter held fixed, text‐derived composition representations produce distinct screening outcomes across 14 electrocatalyst libraries. Embedding model and composition encoding control the trade‐off between candidate reduction and preservation of the best measured current.
Lei Zhang, Markus Stricker
wiley +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Advanced Intelligent Discovery, EarlyView.Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization
AlgorithmsOptimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria.
Adarsh Kesireddy, F. Antonio Medrano
doaj +1 more source
A non compensatory composite index for measuring well-being over time
, 2013 In the last decades, there has been a growing consensus among specialists and researcher that GDP per capita cannot alone explain the well-being in a geographical area.
MAZZIOTTA, Matteo, Pareto, A.
core
Pareto set of different methods on the USPS dataset.
Pareto set of different methods on the USPS dataset.
Hatem A. Khater (18297656) +2 more
core +1 more source
Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training
Advanced Intelligent Systems, EarlyView.The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez +3 more
wiley +1 more source
Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)Pareto set learning (PSL) is an emerging approach for acquiring the complete Pareto set of a multi-objective optimization problem. Existing methods primarily rely on the mapping of preference vectors in the objective space to Pareto optimal solutions in the decision space.
Rongguang Ye +4 more
openaire +3 more sources
Pareto set of different methods on the Banana dataset.
Pareto set of different methods on the Banana dataset.
Hatem A. Khater (18297656) +2 more
core +1 more source