Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

Deep‐learning (DL) methods, in consideration of their excellence in dealing with highly complex structure–performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive‐scal...

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Veröffentlicht in:	Advanced materials (Weinheim) 2022-07, Vol.34 (26), p.e2200908-n/a
Hauptverfasser:	Yu, Songlin, Chai, Haiyang, Xiong, Yuqi, Kang, Ming, Geng, Chengzhen, Liu, Yu, Chen, Yanqiu, Zhang, Yaling, Zhang, Qian, Li, Changlin, Wei, Hao, Zhao, Yuhang, Yu, Fengmei, Lu, Ai
Format:	Artikel
Sprache:	eng
Schlagworte:	additive manufacturing Artificial neural networks Datasets deep learning Feature extraction Learning machine learning material design Material properties porous silicone rubber Silicone rubber
Online-Zugang:	Volltext
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