  High-fidelity, data-driven models that can quickly simulate thermal behavior
during additive manufacturing (AM) are crucial for improving the performance of
AM technologies in multiple areas, such as part design, process planning,
monitoring, and control. However, the complexities of part geometries make it
challenging for current models to maintain high accuracy across a wide range of
geometries. Additionally, many models report a low mean square error (MSE)
across the entire domain (part). However, in each time step, most areas of the
domain do not experience significant changes in temperature, except for the
heat-affected zones near recent depositions. Therefore, the MSE-based fidelity
measurement of the models may be overestimated.
  This paper presents a data-driven model that uses Fourier Neural Operator to
capture the local temperature evolution during the additive manufacturing
process. In addition, the authors propose to evaluate the model using the $R^2$
metric, which provides a relative measure of the model's performance compared
to using mean temperature as a prediction. The model was tested on numerical
simulations based on the Discontinuous Galerkin Finite Element Method for the
Direct Energy Deposition process, and the results demonstrate that the model
achieves high fidelity as measured by $R^2$ and maintains generalizability to
geometries that were not included in the training process.
