CODES
Table of Contents: open source codes:

Project 1: Matlab FEM Damage code  local and gradient nonlocal models

Project 2: Integrated Finite Element Neural Network for nonlocal damage

Project 3: Analysis of error convergence and computational cost of PINNs in nonlocal damage IFENN

Project 4: Integrated Finite Element Neural Network for thermoelasticity
Project 1: Matlab FEM Damage code  local and gradient nonlocal models
Description: Matlab code for FEM models of local and nonlocal gradient damage models. Three solvers included: Displacementbased arclength, Forcebased arclength, and NewtoRaphson. All solvers work with both analytically derived tangent and numerically calculated tangent matrices.
Papers:

Saji, R.P., Pantidis, P., & Mobasher, M. E. (2024). A new displacementcontrolled Arclength method for damage mechanics problems, Compitational Mechanics (Link)
Open source code:
Project 2: Integrated Finite Element Neural Network for nonlocal damage
Description: The codes used in the below study to train and test a neural networks for nonlocal damage mechanics IFENN. The damage mechanics solver is provided in Project 1.
Papers:

Pantidis, P., & Mobasher, M. E. (2023). Integrated Finite Element Neural Network (IFENN) for nonlocal continuum damage mechanics. Computer Methods in Applied Mechanics and Engineering, 404, 115766. (Link)

Pantidis, P., Eldababy, H., Abueidda, D., & Mobasher, M. E. (2024). IFENN with Temporal Convolutional Networks: expediting the loadhistory analysis of nonlocal gradient damage propagation. Computer Methods in Applied Mechanics and Engineering, 425, 116940. (Link)
Open source code:

Sequential MLP model (Pantidis & Mobasher, 2023)

PITCN model (Pantidis et al., 2024)
Project 3: Analysis of error convergence and computational cost of PINNs in nonlocal damage IFENN
Description: Simulation data and python code used in the below study to assess the perofomance of PINNs used for nonlocal damage IFENN model. The analysis considers maximuizing accuarcy, decreasig computational cost, and avoiding trivial solutions. The damage mechanics solver is provided in Project 1.
Papers:

Pantidis, P., Eldababy, H., Tagle, C. M., & Mobasher, M. E. (2023). Error convergence and engineeringguided hyperparameter search of PINNs: towards optimized IFENN performance. Computer Methods in Applied Mechanics and Engineering, 414, 116160. (Link)
Open source code:

Model, analysis, and data (Pantidis et al., 2023)
Project 4: Integrated Finite Element Neural Network for thermoelasticity
Description: The codes used in the below study to train and test a neural networks for thermoelasticity IFENN.
Papers:

Abueidda, D. W., & Mobasher, M. E. (2024). IFENN for thermoelasticity based on physicsinformed temporal convolutional network (PITCN). Computational Mechanics. (Link)
Open source code:

PITCN model (Abueidda & Mobasher, 2024)