A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery

By Zhongqing Han, Rahul, Suvranu De. 2019

21 citations recorded by the source corpus.

Multiphysics modeling of evolving topology in the electrosurgical dissection of soft hydrated tissues is a challenging problem, requiring heavy computational resources. In this paper, we propose a hybrid approach that leverages the regressive capabilities of deep convolutional neural networks (CNN) with the precision of conventional solvers to accelerate Multiphysics computations. The electro-thermal problem is solved using a finite element method (FEM) with a Krylov subspace-based iterative solver and a deflation-based block preconditioner. The mechanical deformation induced by evaporation of intra- and extracellular water is obtained using a CNN model. The CNN is trained using a supervised learning framework that maps the nonlinear relationship between the micropore pressure and deformation field for a given tissue topology. The simulation results show that the hybrid approach is significantly more computationally efficient than a FEM-based solution approach using a block-preconditioned Krylov subspace solver and a parametric solution approach using a proper generalized decomposition (PGD) based reduced order model. The accuracy of the hybrid approach is comparable to the ground truth obtained using a standard multiphysics solver. The hydrid approach overcomes the limitations of end-to-end learning including the need for massive datasets for training the network.

Subfields: Biomedical Engineering, Computational Mechanics, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

  1. Paris Perdikaris
  2. G. Em Karniadakis
  3. Ellen Kuhl
  4. William R. Cannon

In Computational Mechanics

  1. G. Em Karniadakis
  2. Paris Perdikaris
  3. Ellen Kuhl
  4. Suvranu De
  5. Maizar Raissi
  6. Lu Lu
  7. Zhiping Mao
  8. Jian-Xun Wang

In Statistical and Nonlinear Physics

  1. G. Em Karniadakis
  2. Paris Perdikaris
  3. Maizar Raissi
  4. Ellen Kuhl
  5. Suvranu De
  6. Savador Dura-Bernal
  7. William W. Lytton
  8. Linda Petzold

Citations in the corpus