Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

18 Feb 2020 Zhang Ruiyang Liu Yang Sun Hao

This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which boosts the learning within a feasible solution space... (read more)

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  • COMPUTATIONAL ENGINEERING, FINANCE, AND SCIENCE
  • SIGNAL PROCESSING