Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning. (arXiv:2210.03762v1 [physics.app-ph])

We consider quantitative analyses of spectral data using laser-induced
breakdown spectroscopy. We address the small size of training data available,
and the validation of the predictions during inference on unknown data. For the
purpose, we build robust calibration models using deep convolutional multitask
learning architectures to predict the concentration of the analyte, alongside
additional spectral information as auxiliary outputs. These secondary
predictions can be used to validate the trustworthiness of the model by taking
advantage of the mutual dependencies of …

app arxiv augmentation breakdown data multitask learning physics predictions simulation synthetic data