Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database

• URL: http://arxiv.org/abs/2303.14179v1
• Date: Thu, 23 Mar 2023 04:14:05 GMT
• Title: Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database
• Authors: Su Chen, Xianwei Liu, Lei Fu, Suyang Wang, Bin Zhang, Xiaojun Li
• Abstract summary: Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method is proposed to automatically discover mathematical equation operators as symbols.
• Score: 4.059252581613122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method based on the Nest Generation Attenuation-West2 database is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to distill a concise and interpretable explicit characterization of complex systems of ground motions. In addition to the basic variables retrieved from previous GMMs, the current PISL incorporates two a priori physical conditions, namely, distance and amplitude saturation. GMMs developed using the PISL, an empirical regression method (ERM), and an artificial neural network (ANN) are compared in terms of residuals and extrapolation based on obtained data of peak ground acceleration and velocity. The results show that the inter- and intra-event standard deviations of the three methods are similar. The functional form of the PISL is more concise than that of the ERM and ANN. The extrapolation capability of the PISL is more accurate than that of the ANN. The PISL-GMM used in this study provide a new paradigm of regression that considers both physical and data-driven machine learning and can be used to identify the implied physical relationships and prediction equations of ground motion variables in different regions.

Related papers

• MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data [22.262191225577244]
  We explore whether a similar approach can be applied to scientific foundation models (SFMs) We collect low-cost physics-informed neural network (PINN)-based approximated prior data in the form of solutions to partial differential equations (PDEs) constructed through an arbitrary linear combination of mathematical dictionaries. We provide experimental evidence on the one-dimensional convection-diffusion-reaction equation, which demonstrate that pre-training remains robust even with approximated prior data.
  arXiv  Detail & Related papers  (2024-10-09T00:52:00Z)
• Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines [0.0]
  This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation System (C-MAPSS) data as the main data for this paper. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods.
  arXiv  Detail & Related papers  (2024-06-21T19:55:34Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• 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)
• Brain Model State Space Reconstruction Using an LSTM Neural Network [2.603611220111237]
  This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures.
  arXiv  Detail & Related papers  (2023-01-20T02:02:54Z)
• Deep learning for full-field ultrasonic characterization [7.120879473925905]
  This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform. Two logics, namely the direct inversion and physics-informed neural networks (PINNs), are explored.
  arXiv  Detail & Related papers  (2023-01-06T05:01:05Z)
• Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials [0.0]
  We develop an ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage. The predictions achieve a good level of accuracy, similar to other ML algorithms.
  arXiv  Detail & Related papers  (2022-10-19T12:20:07Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)
• DeepGMR: Learning Latent Gaussian Mixture Models for Registration [113.74060941036664]
  Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics. In this paper, we introduce Deep Gaussian Mixture Registration (DeepGMR), the first learning-based registration method. Our proposed method shows favorable performance when compared with state-of-the-art geometry-based and learning-based registration methods.
  arXiv  Detail & Related papers  (2020-08-20T17:25:16Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)

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.