Nov 24, 2022 · This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various ...

Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are ...

In this review, the details of physics-guided deep learning are elucidated, and a structured overview of PGDL with respect to data scarcity in various ...

Mar 21, 2023 · Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. It can be used for any ...

Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. It can be used for any systems that are ...

Apr 14, 2023 · This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced ...

This paper aims to bridge this gap and explore the interface between deep learning and system reliability assessment by expanding and adapting recent advances.

These models often require a large amount of high-quality training data with sufficient range to avoid excessive extrapolation and produce reliable predictions.

Additionally, the PGML framework will also be useful for phys- ical systems where the data are scarce. For example, the generation of training data is ...

This paper explains how to use physical principles in feature engineering to improve machine learn- ing outcomes. Equipped with energy, mass, and.