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

02/18/2020
by   Ruiyang Zhang, et al.
Northeastern University
0

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. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.

READ FULL TEXT
04/09/2019

Physics-Informed Echo State Networks for Chaotic Systems Forecasting

We propose a physics-informed Echo State Network (ESN) to predict the ev...
09/17/2019

Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling

Seismic events, among many other natural hazards, reduce due functionali...
02/08/2019

Differentiable Physics-informed Graph Networks

While physics conveys knowledge of nature built from an interplay betwee...
09/28/2021

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning

Integrating physical inductive biases into machine learning can improve ...
12/15/2021

Leveraging the structure of dynamical systems for data-driven modeling

The reliable prediction of the temporal behavior of complex systems is r...

Please sign up or login with your details

Forgot password? Click here to reset