Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of PEML methods, expressed across the defi...
Saved in:
| Main Authors: | Marcus Haywood-Alexander, Wei Liu, Kiran Bacsa, Zhilu Lai, Eleni Chatzi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2024-01-01
|
| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673624000339/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Response estimation and system identification of dynamical systems via physics-informed neural networks
by: Marcus Haywood-Alexander, et al.
Published: (2025-04-01) -
Toward a physics-guided machine learning approach for predicting chaotic systems dynamics
by: Liu Feng, et al.
Published: (2025-01-01) -
Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator
by: Nafisa Mehtaj, et al.
Published: (2025-02-01) -
Dynamic displacement reconstruction of bridge based on physics-informed recurrent neural network
by: Yi Tao, et al.
Published: (2025-03-01) -
A review on physics-informed machine learning for monitoring metal additive manufacturing process
by: Shoulan Yang, et al.
Published: (2024-06-01)