mlsanityData to 14 Oct 2026

Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)

arXiv 2406.03172 ↗First posted 5 Jun 2024cs.LGcited by 9

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Abstract (arXiv)

We propose a new physics-informed neural network framework, IDPINN, based on the enhancement of initialization and domain decomposition to improve prediction accuracy. We train a PINN using a small dataset to obtain an initial network structure, including the weighted matrix and bias, which initializes the PINN for each subdomain. Moreover, we leverage the smoothness condition on the interface to enhance the prediction performance. We numerically evaluated it on several forward problems and demonstrated the benefits of IDPINN in terms of accuracy.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

24 of 49 references.

References · 24

  1. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
  2. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  3. 2020hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decompositioncited by 236
  4. 2022A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networkscited by 236
  5. 2021Gradient-enhanced physics-informed neural networks for forward and inverse PDE problemscited by 222
  6. 2021Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equationscited by 145
  7. 2019Transfer learning enhanced physics informed neural network for phase-field modeling of fracturecited by 116
  8. 2020Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEscited by 116
  9. 2023Residual-based attention in physics-informed neural networkscited by 72
  10. 2020Physics-informed neural network for ultrasound nondestructive quantification of surface breaking crackscited by 68
  11. 2020Transfer learning based multi-fidelity physics informed deep neural networkcited by 68
  12. 2022Failure-informed adaptive sampling for PINNscited by 64
12 more
  1. 2021When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?cited by 58
  2. 2020Physics-informed deep learning for incompressible laminar flowscited by 54
  3. 2022Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systemscited by 54
  4. 2020Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structurescited by 52
  5. 2021One-Shot Transfer Learning of Physics-Informed Neural Networkscited by 49
  6. 2021Physics informed neural networks for continuum micromechanicscited by 46
  7. 2022Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarioscited by 46
  8. 2022Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodologycited by 44
  9. 2023Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategiescited by 20
  10. 2022SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domaincited by 16
  11. 2022Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Supportcited by 6
  12. 2023Physics-informed neural networks in the recreation of hydrodynamic simulations from dark mattercited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 2.

9 papers.

  1. 2025Complex Physics-Informed Neural Networkcited by 7
  2. 2025Convolution-weighting method for the physics-informed neural network: A Primal-Dual Optimization Perspectivecited by 6
  3. 2024Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solverscited by 4
  4. 2025BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solverscited by 1
  5. 2025Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Modelscited by 1
  6. 2026Lightweight Geometric Adaptation for Training Physics-Informed Neural Networkscited by 1
  7. 2025Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networkscited by 0
  8. 2025AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networkscited by 0
  9. 2026From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNscited by 0