Abstract: The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data.

This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

This study introduces a novel ML framework that integrates a Conditional Variational Autoencoder (CVAE) for realistic data augmentation, aiming to enhance model robustness and generalization for the ...

Based on the solution approach, the methods can be divided into PINNs (Neural Networks for solving equations) and data-driven surrogate models. The latter can be further categorized based on the type ...