ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- Sustained and Enhanced Nucleate Boiling Using Hierarchical Architectures at Large Superheats.Exploration (Beijing, China) · 2025Article
- Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Authors and funding
6 authors.
Funding
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 this strategy leads to the discovery of the alloy with latent heat of -36.08 J g
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