Core Concepts
This paper introduces ALEMO, an active learning-enhanced evolutionary multi-objective optimization algorithm, to accelerate the design of fractured geothermal systems by integrating machine learning with hydrothermal simulations, achieving a speed-up of 1-2 orders of magnitude compared to traditional methods.
Chen, G., Jiao, J.J., Liu, Q., Wang, Z., & Jin, Y. (Year). Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal Systems.
This paper aims to address the challenge of computationally expensive multi-objective optimization in designing fractured geothermal systems (EGS) by developing a novel algorithm that leverages machine learning to accelerate the optimization process.