Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal data

Enthalten in
Neural networks 
Volume
156
Start Page
123
End Page
134
Citation
Neural Networks 156: 123-134 (2022-12)
Publisher DOI
10.1016/j.neunet.2022.09.023
Scopus ID
2-s2.0-85139865358
ArXiv ID
2204.06843v2
Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, increasing weight initialization robustness, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto–Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.
Schlagworte
Deep learning
Error metric
Loss function
Nonlinear dynamics
Similarity
Spatio-temporal dynamics
DDC Class
004: Informatik
530: Physik