Physics-guided machine learning (ML) is an emerging paradigm that combines both data-driven ML models and physics-based models together to fully take advantage of the data discovery ability of ML without losing the valuable physics/domain knowledge. This paper proposes a novel physics-guided ML method based on recurrent neural network (RNN) and multilayer perceptron (MLP) for structural dynamics simulation. The key idea is to integrate the underlying physics of structural dynamics into data-enabled ML models to ‘guide’ the training and prediction of ML models. First, structural dynamics formulation and the use of data-driven RNN and MLP for modeling dynamical systems are briefly reviewed, which leads to the development of the proposed physics-guided ML model. Physics-guided ML model contains physics-based layers to encode the known physics and data-driven layers to approximate the unknown relationships. Thus, the data-driven RNN and MLP are augmented with existing physics knowledge for better performance in simulations. Following this, several numerical case studies for structural dynamics are presented to demonstrate the proposed methodology. It is observed that: (1) compared with purely data-driven ML method, the proposed physics-guided ML method has better generalization ability and reduced training costs; (2) compared with physics-based modeling, the proposed method has improved computational efficiency and can handle partially unknown physics. Finally, conclusions and future works are presented based on the proposed study.
|Original language||English (US)|
|Journal||Engineering Applications of Artificial Intelligence|
|State||Published - Nov 2020|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering