### Research Objective
The main objective of the research is to improve the generalizability of causal physics-informed neural networks (PINNs) for simulating the dynamics of beams on elastic foundations. This is achieved by integrating transfer learning into the PINN framework to address the limitations of conventional PINNs, particularly in simulation for large space-time domains and varying initial conditions.
### Type of Research
The research is an applied study focused on the development and validation of advanced computational methods in structural engineering. It combines elements of theoretical development (modification of the PINN loss function) and empirical validation (numerical experiments on Euler-Bernoulli and Timoshenko beams).
### Method of Data Collection
For validating the proposed methodology closed analytical form is utilized. This closed form solution is exciplicitly mentioned as exact solution function. These analytical solution is utilize for simulating the dynamics of beams on elastic foundations using both the traditional PINNs and the proposed transfer learning-based causal PINN framework.
### Type of Data
The type of data used in this research includes:
1. Training Data for Sequential experiments: Solutions (Data) to partial differential equations (PDEs) modeling the dynamics of Euler-Bernoulli and Timoshenko beams are simulated using physics informed neural networks.
### File type/extension included in the folder
All codes are implemented using python jupyter notebook(.ipynb), Trained model files (.pkl, .pth), Log files (.log) showing the results at every iteration, and (.sh) files to execute on the cluster, .pdf and .png are figures which are used in the main paper.
For Causal PINN experiments well-posed physical equations of Euler-Bernoulli and Timoshenko is utilized.