Fracture modeling using Physics Informed Neural Network

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The Physics Informed Neural Networks are trained to solve supervised learning problems while respecting any given law of physics described by general non-linear partial differential equations. The developed PINN approach takes a different path by minimizing the variational energy of the system to resolve the crack path within the framework of phase field modeling approach. One major advantage of the variational energy formulation resides in the fact that it requires derivatives of lower order than in the conventional residual minimization approach. Hence, this approach is computationally efficient. The concept of Transfer Learning and Adaptive Refinement Technique has been integrated with the developed PINN approach.

Companion paper:

  1. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
  2. An energy approach to the solution of partial differential equations in computational mechanics via machine learning:Concepts, implementation and applications
  3. Adaptive fourth-order phase field analysis using deep energy minimization

Adaptive phase field modeling of fracture using Isogeometric analysis

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Companion paper: Adaptive fourth-order phase field analysis for brittle fracture

Dual mesh approach of fracture modeling using Isogeometric analysis

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Companion paper: Adaptive phase field analysis with dual hierarchical meshes for brittle fracture