A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
A Pointwise Evaluation Metric to Visualize Errors in Machine Learning Surrogate Models
[chapter]
2021
Frontiers in Artificial Intelligence and Applications
Numerical simulation is widely used to study physical systems, although it can be computationally too expensive. To counter this limitation, a surrogate may be used, which is a high-performance model that replaces the main numerical model by using, e.g., a machine learning (ML) regressor that is trained on a previously generated subset of possible inputs and outputs of the numerical model. In this context, inspired by the definition of the mean squared error (MSE) metric, we introduce the
doi:10.3233/faia210386
fatcat:tajvmcw7gjgwdjofc6boxfr4ea