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This package is designed to give you both the 이론적인 understanding and 현실적인 implementation skills required to build intelligent constitutive models in Abaqus using deep learning frameworks.

Lession: Theorical Undrestanding

You will first learn how neural networks can replace traditional closed-form constitutive equations by approximating material energy potentials. The course explains how strain energy density functions can be learned from data and then used to derive stress responses and tangent operators required in finite element analysis.

A strong focus is placed on tensor mathematics and continuum mechanics fundamentals, ensuring that the neural network outputs remain physically meaningful within nonlinear simulations. You will also understand how gradients and second-order derivatives are obtained using PyTorch automatic differentiation. This is essential because Abaqus UMAT requires not only stress updates but also a consistent DDSDDE tangent matrix for robust convergence.

Workshop1: Practical Implementation

A major practical section of the course covers the offline deployment strategy. You will learn how to extract trained weights and biases from PyTorch, translate them into Fortran arrays, and manually reconstruct the network’s forward propagation inside a UMAT subroutine. This enables AI-driven constitutive behavior without requiring Python during Abaqus execution.

In the workshop section, you will implement a Fully Connected Neural Network to emulate Neo-Hookean hyperelasticity, verify stress and stiffness accuracy, and run a single-element benchmark model in Abaqus. Finally, you will compare computational speed, numerical stability, and predictive capability between the neural-network UMAT and the standard Abaqus material model.

By the end of the package, you will have a complete roadmap for integrating deep learning material models into industrial finite element workflows.

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