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Point-Deeponet: Predicting nonlinear fields on non-Parametric geometries under variable load conditions.

Jangseop Park1, Namwoo Kang2

  • 1Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon, 34051, South Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|January 16, 2026
PubMed
Summary
This summary is machine-generated.

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Point-wise conditional diffusion models for physical systems with shape variations: Applications to spatio-temporal and large-scale systems.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Point-DeepONet accelerates nonlinear structural analysis using deep learning. This novel surrogate model accurately predicts 3D displacement and stress fields for complex geometries, offering significant speedups over traditional methods.

Area of Science:

  • Computational Engineering
  • Artificial Intelligence
  • Structural Mechanics

Background:

  • Nonlinear structural analyses rely on computationally intensive finite element simulations.
  • Current deep learning surrogates face challenges with complex 3D geometries and variable loads.

Purpose of the Study:

  • To develop a high-fidelity, operator-learning-based surrogate model for nonlinear structural analysis.
  • To enable rapid prediction of physical response fields for complex, non-parametric engineering problems.

Main Methods:

  • Integration of PointNet for geometric representation learning from point clouds into the DeepONet framework.
  • Synergistic fusion of geometric embeddings and load conditions to predict 3D displacement and von Mises stress fields.
  • Training on a large-scale dataset and validation on unseen load conditions.
Keywords:
Deep operator network (deeponet)Non-parametric 3D geometryNonlinear structure analysisPointnetSurrogate modeling

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Main Results:

  • Point-DeepONet achieved high accuracy, with R² values up to 0.987 for displacement and 0.923 for von Mises stress.
  • The model demonstrated excellent generalization to randomly oriented, unseen load directions.
  • Predictions were achieved in seconds, approximately 400 times faster than traditional finite element analyses.

Conclusions:

  • Point-DeepONet offers a significant advancement in computational efficiency and accuracy for nonlinear structural analysis.
  • The model's ability to handle complex geometries and variable loads opens new possibilities for design optimization and real-time control.
  • This approach shows great potential for accelerating complex engineering workflows.