A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content

• URL: http://arxiv.org/abs/2305.08102v1
• Date: Sun, 14 May 2023 08:33:11 GMT
• Title: A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
• Authors: Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux, Raimund Rolfes
• Abstract summary: We propose a deep learning (DL)-based model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticles with moisture content. A long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force-displacement response of nanoparticle/ epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.

Related papers

• Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework [0.0]
  A hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics-based knowledge contained in the models used in the full-order micromodel by embedding them in a neural network.
  arXiv  Detail & Related papers  (2024-04-05T12:40:03Z)
• A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites [0.0]
  This work proposes a physics-informed deep learning (PIDL)-based model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticles-filled epoxies under various ambient conditions. The PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.
  arXiv  Detail & Related papers  (2024-03-27T07:22:32Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• 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)
• Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy [3.613625739845355]
  We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition. It may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.
  arXiv  Detail & Related papers  (2022-05-23T15:50:24Z)
• A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements [3.65211252467094]
  We present a data-driven correlation to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios. A material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve leaflet. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material properties learned implicitly from the data and naturally embedded in the network parameters.
  arXiv  Detail & Related papers  (2022-04-01T04:56:41Z)
• 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)
• Dynamic fracture of a bicontinuously nanostructured copolymer: A deep learning analysis of big-data-generating experiment [0.0]
  We report the dynamic fracture toughness as well as the cohesive parameters of a bicontinuously nanostructured copolymer, polyurea, under an extremely high crack-tip loading rate. For the first time, the dynamic cohesive parameters of polyurea have been successfully obtained by the pre-trained CNN architecture.
  arXiv  Detail & Related papers  (2021-12-03T15:31:59Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)

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.