Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures
- URL: http://arxiv.org/abs/2402.17992v3
- Date: Mon, 29 Apr 2024 14:47:42 GMT
- Title: Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures
- Authors: R. Bailey Bond, Pu Ren, Jerome F. Hajjar, Hao Sun,
- Abstract summary: PiML method integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures.
Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results.
Result handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
- Score: 6.483318568088176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: There is growing interest in using machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional simulations. Purely data-driven strategies often face limitations in model robustness, interpretability, and dependency on extensive data. To address these challenges, this paper introduces a novel physics-informed machine learning (PiML) method that integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures. The approach constrains the ML model's solution space within known physical bounds through three main features: dimensionality reduction via combined model order reduction and wavelet analysis, long short-term memory (LSTM) networks, and Newton's second law. Dimensionality reduction addresses structural systems' redundancy and boosts efficiency while extracting essential features through wavelet analysis. LSTM networks capture temporal dependencies for accurate time-series predictions. Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results. These attributes allow for model training with sparse data, enhancing accuracy, interpretability, and robustness. Furthermore, a dataset of archetype steel moment resistant frames under seismic loading, available in the DesignSafe-CI Database [1], is considered for evaluation. The resulting metamodel handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
Related papers
- Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.
Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
arXiv Detail & Related papers (2025-02-16T23:13:55Z) - Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear Dynamics Based Features [5.652228574188242]
This paper introduces a novel approach that departs from standard techniques by uncovering information from nonlinear dynamical modeling and embedding it in data-based models.
By explicitly incorporating nonlinear dynamic phenomena through perturbation methods, the predictive capabilities are more realistic and insightful compared to knowledge obtained from brute-force numerical simulations.
arXiv Detail & Related papers (2025-01-21T02:38:28Z) - Physics-Informed Deep Learning of Rate-and-State Fault Friction [0.0]
We develop a multi-network PINN for both the forward problem and for direct inversion of nonlinear fault friction parameters.
We present the computational PINN framework for strike-slip faults in 1D and 2D subject to rate-and-state friction.
We find that the network for the parameter inversion at the fault performs much better than the network for material displacements to which it is coupled.
arXiv Detail & Related papers (2023-12-14T23:53:25Z) - Discovering Interpretable Physical Models using Symbolic Regression and
Discrete Exterior Calculus [55.2480439325792]
We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models.
DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems.
We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
arXiv Detail & Related papers (2023-10-10T13:23:05Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate
Model Predictive Trajectory Tracking [76.27433308688592]
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation.
We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience.
Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
arXiv Detail & Related papers (2022-06-07T13:51:35Z) - Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
We consider the impact of the training set and its structure on the quality of the long-term prediction.
We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
arXiv Detail & Related papers (2021-12-15T20:09:20Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Physics-guided Deep Markov Models for Learning Nonlinear Dynamical
Systems with Uncertainty [6.151348127802708]
We propose a physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM)
The proposed framework takes advantage of the expressive power of deep learning, while retaining the driving physics of the dynamical system.
arXiv Detail & Related papers (2021-10-16T16:35:12Z) - DynNet: Physics-based neural architecture design for linear and
nonlinear structural response modeling and prediction [2.572404739180802]
In this study, a physics-based recurrent neural network model is designed that is able to learn the dynamics of linear and nonlinear multiple degrees of freedom systems.
The model is able to estimate a complete set of responses, including displacement, velocity, acceleration, and internal forces.
arXiv Detail & Related papers (2020-07-03T17:05:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.