Deep material network for homogenization of piezoelectric composites

• URL: http://arxiv.org/abs/2606.22566v1
• Date: Sun, 21 Jun 2026 16:08:24 GMT
• Title: Deep material network for homogenization of piezoelectric composites
• Abstract summary: This work proposes a piezoelectric deep material network to efficiently homogenize two-phase piezoelectric composites.<n>The proposed framework embeds the governing electromechanical homogenization relations directly into the network architecture.<n> Numerical examples show that the proposed PDMN achieves high predictive accuracy while reducing the computational cost by more than three orders of magnitude compared with DNS.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical performance can be tailored by combining constituent phases and microstructural architecture. However, conventional computational homogenization based on direct numerical simulation (DNS) is computationally expensive, particularly for multiscale simulations and material design tasks that require repeated homogenization analyses. To address this limitation, this work proposes a piezoelectric deep material network (PDMN) to efficiently homogenize two-phase piezoelectric composites. The proposed framework embeds the governing electromechanical homogenization relations directly into the network architecture, yielding a physics-informed, semi-analytical surrogate that explicitly captures the two-way coupling between the mechanical and electrical fields across constituent phases. The network is trained offline on linear electroelastic datasets and, through a fully coupled Newton--Raphson solution with a consistent electromechanical tangent, subsequently used for efficient online prediction under broader constitutive settings, including nonlinear electroelasticity and history-dependent responses. The framework is validated on two-phase composites of polyvinylidene fluoride (PVDF) and lithium niobate (LiNbO$_3$) with reversed phase arrangements under nonlinear electroelastic loading, and on a viscoelastic--piezoelectric composite exhibiting coupled stress relaxation. Numerical examples show that the proposed PDMN achieves high predictive accuracy while reducing the computational cost by more than three orders of magnitude compared with DNS. The proposed framework, therefore, provides an efficient and reliable surrogate for the multiscale analysis and design of piezoelectric composites.

Related papers

• On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks [0.9158247555026059]
  Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures.<n>The mechanical response of SFTs is governed by mesoscale interactions among fiber orientation, spatial clustering, and manufacturing-induced porosity.<n>A data-driven surrogate framework is proposed to predict the mechanical behavior of AM-CM SFTs.
  arXiv  Detail & Related papers  (2026-06-27T16:18:48Z)
• Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems [4.636700312219189]
  This study proposes a Residual Attention Physics-Informed Neural Network (RA-PINN) framework for the unified solution of coupled velocity, pressure, electric-potential, and temperature fields.<n>We evaluate RA-PINN across four representative energy-relevant benchmarks: constant-coefficient coupling, indirect pressure-gauge constraints, temperature-dependent transport, and interface consistency.
  arXiv  Detail & Related papers  (2026-03-24T13:44:46Z)
• A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization [0.0]
  Physics-Informed Neural Networks present a novel approach in SciML that integrates physical laws in the form of partial differential equations directly into the NN.<n>This work studies the application of PINNs to solve a one dimensional coupled electro-elastodynamic system modeling linear piezoelectricity in stress-charge form.
  arXiv  Detail & Related papers  (2026-02-14T14:52:08Z)
• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation.<n>This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials [0.0]
  Materials with high-dielectric constants easily polarize under external electric fields, allowing them to perform essential functions in modern electronic devices. We present a workflow that combines element substitution, ML pre-screening, ab initio simulation and human expert intuition to explore the vast space of unknown materials. We report the first high-purity synthesis and characterization of Bi2Zr2O7 with a band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of our multi-objective search.
  arXiv  Detail & Related papers  (2024-01-11T11:38:20Z)
• Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing [61.27691515336054]
  In this study, we tackle the issue of high-performance electrodes for desired battery application conditions. We propose a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.
  arXiv  Detail & Related papers  (2023-07-07T13:48:50Z)
• Simulating Spin-Orbit Coupling With Quasidegenerate N-Electron Valence Perturbation Theory [77.34726150561087]
  We present the first implementation of spin-orbit coupling effects in SO-QDNEVPT2. The accuracy of these methods is tested for the group 14 and 16 hydrides, 3d and 4d transition metal ions, and two actinide dioxides.
  arXiv  Detail & Related papers  (2022-11-11T20:03:37Z)
• Driving Force and Nonequilibrium Vibronic Dynamics in Charge Separation of Strongly Bound Electron-Hole Pairs [59.94347858883343]
  We study the dynamics of charge separation in one, two and three-dimensional donor-acceptor networks. This allows us to identify the precise conditions in which underdamped vibrational motion induces efficient long-range charge separation.
  arXiv  Detail & Related papers  (2022-05-11T17:51:21Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Comparison of Optical Response from DFT Random Phase Approximation and Low-Energy Effective Model: Strained Phosphorene [0.0]
  We compare and contrast the dispersive permittivity tensor, using both a low-energy effective model and density functional theory (DFT) Our results suggest that the random-phase approximation employed in widely used DFT packages should be revisited and improved to be able to predict these fundamental electronic characteristics of a given material with confidence.
  arXiv  Detail & Related papers  (2021-09-01T18:00:06Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries [0.0]
  This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. A comparison between the real and synthetic data is performed in terms of the morphological properties. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions.
  arXiv  Detail & Related papers  (2020-02-17T17:38:27Z)

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.