Multi-fidelity surrogate modeling for chemical processes with physics-informed neural networks

會議論文

Multi-fidelity surrogate modeling for chemical processes with physics-informed neural networks

Yu-Ting Liu, Chuan-Yu Wu, Tao Chen 和遠姚

33rd European Symposium on Computer Aided Process Engineering (ESCAPE33)

Athens, Greece, 18/06/2023–21/06/2023

06/2023

摘要

deep learning;multi-fidelity;physics-informed neural network;surrogate modeling

相關連結

指標

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詳細資料

題名: Multi-fidelity surrogate modeling for chemical processes with physics-informed neural networks
創作者：: Yu-Ting Liu (作者)
Chuan-Yu Wu (作者)
Tao Chen (作者)
遠姚 (作者)
會議: Athens, Greece, 18/06/2023–21/06/2023
學術資源類型: 會議論文
出版日期: 06/2023
出版資訊: 33rd European Symposium on Computer Aided Process Engineering (ESCAPE33)
語言: 英語