A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks

• URL: http://arxiv.org/abs/2211.12459v1
• Date: Tue, 22 Nov 2022 18:16:16 GMT
• Title: A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
• Authors: Roberto Perera, Vinamra Agrawal
• Abstract summary: We use transfer learning approaches to circumvent the need for retraining with large datasets. We demonstrate ACCURATE's ability to predict crack growth and stress evolution with high accuracy for unseen cases. The framework provides a universal computational fracture mechanics model that can be easily modified or extended in future work.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their recent success, machine learning (ML) models such as graph neural networks (GNNs), suffer from drawbacks such as the need for large training datasets and poor performance for unseen cases. In this work, we use transfer learning (TL) approaches to circumvent the need for retraining with large datasets. We apply TL to an existing ML framework, trained to predict multiple crack propagation and stress evolution in brittle materials under Mode-I loading. The new framework, ACCelerated Universal fRAcTure Emulator (ACCURATE), is generalized to a variety of crack problems by using a sequence of TL update steps including (i) arbitrary crack lengths, (ii) arbitrary crack orientations, (iii) square domains, (iv) horizontal domains, and (v) shear loadings. We show that using small training datasets of 20 simulations for each TL update step, ACCURATE achieved high prediction accuracy in Mode-I and Mode-II stress intensity factors, and crack paths for these problems. %case studies (i) - (iv). We demonstrate ACCURATE's ability to predict crack growth and stress evolution with high accuracy for unseen cases involving the combination of new boundary dimensions with arbitrary crack lengths and crack orientations in both tensile and shear loading. We also demonstrate significantly accelerated simulation times of up to 2 orders of magnitude faster (200x) compared to an XFEM-based fracture model. The ACCURATE framework provides a universal computational fracture mechanics model that can be easily modified or extended in future work.

Related papers

• Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures [6.483318568088176]
  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.
  arXiv  Detail & Related papers  (2024-02-28T02:16:03Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)
• Rail Crack Propagation Forecasting Using Multi-horizons RNNs [0.46040036610482665]
  The prediction of rail crack length propagation plays a crucial role in the maintenance and safety assessment of materials and structures. Traditional methods rely on physical models and empirical equations such as Paris law. In recent years, machine learning techniques, particularly Recurrent Neural Networks (RNNs), have emerged as promising methods for time series forecasting.
  arXiv  Detail & Related papers  (2023-09-04T12:44:21Z)
• Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence [0.0]
  We investigate how an often overlooked processing step affects the quality of an emulator's predictions. We implement ML architectures from a class of methods called reservoir computing: (1) a form of spatial Vector Autoregression (N VAR), and (2) an Echo State Network (ESN) In all cases, subsampling the training data consistently leads to an increased bias at small scales that resembles numerical diffusion.
  arXiv  Detail & Related papers  (2023-04-28T21:34:53Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns [77.34726150561087]
  We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers. Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
  arXiv  Detail & Related papers  (2022-05-19T08:37:56Z)
• A physics-informed variational DeepONet for predicting the crack path in brittle materials [3.1196544696082613]
  We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis. V-DeepONet is trained to map the initial configuration of the defect to the relevant fields of interests. We demonstrate the effectiveness of V-DeepOnet through two benchmarks of brittle fracture, and we verify its accuracy using results from high-fidelity solvers.
  arXiv  Detail & Related papers  (2021-08-16T05:31:05Z)
• StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials [6.245804384813862]
  Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. "StressNet" is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. The proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds.
  arXiv  Detail & Related papers  (2020-11-20T05:49:12Z)
• One-step regression and classification with crosspoint resistive memory arrays [62.997667081978825]
  High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge. One-step learning is supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. Results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
  arXiv  Detail & Related papers  (2020-05-05T08:00:07Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.