[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
Partial differential equations meet deep neural networks: A survey
A framework for machine learning of model error in dynamical systems
The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …
widespread interest in many disciplines. We present a unifying framework for blending …
Leveraging viscous Hamilton–Jacobi PDEs for uncertainty quantification in scientific machine learning
Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful
predictive power of SciML with methods for quantifying the reliability of the learned models …
predictive power of SciML with methods for quantifying the reliability of the learned models …
Machine learning for elliptic pdes: Fast rate generalization bound, neural scaling law and minimax optimality
In this paper, we study the statistical limits of deep learning techniques for solving elliptic
partial differential equations (PDEs) from random samples using the Deep Ritz Method …
partial differential equations (PDEs) from random samples using the Deep Ritz Method …