MAEO is a new ensemble framework that runs NSGA-III, CTAEA, AGEMOEA2 and SPEA2 in parallel islands with parameter-free hypervolume assessment and strict Pareto-rank selection, showing competitive or better results on DTLZ/ZDT benchmarks and identifying improved nuclear reactor designs.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Active learning with physics-informed surrogates achieves comparable accuracy for a glycol heat exchanger digital twin using only one-fifth the high-fidelity simulation trajectories needed by random sampling.
citing papers explorer
-
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
MAEO is a new ensemble framework that runs NSGA-III, CTAEA, AGEMOEA2 and SPEA2 in parallel islands with parameter-free hypervolume assessment and strict Pareto-rank selection, showing competitive or better results on DTLZ/ZDT benchmarks and identifying improved nuclear reactor designs.
-
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
Active learning with physics-informed surrogates achieves comparable accuracy for a glycol heat exchanger digital twin using only one-fifth the high-fidelity simulation trajectories needed by random sampling.