Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.

ABSTRACT: Shape memory alloys (SMAs) with large latent heat absorbed/released during phase transformation at elevated temperatures benefit their potential application on thermal energy storage (TES) in high temperature environment like power plants, etc. The desired alloys can be designed quickly by searching the vast component space of doped NiTi-based SMAs via data-driven method, while be challenging with the noisy experimental data. A noise-aware active learning strategy is proposed to accelerate the design of SMAs with large latent heat at elevated phase transformation temperatures based on noisy data. The optimal noise level is estimated by minimizing the model error with incorporation of a range of noise levels as noise hyper-parameters into the noise-aware Kriging model. The employment of thi

SUBMITTER: Tian Y 

PROVIDER: S-EPMC11600200 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature