Physics-informed deep learning for one-dimensional consolidation
Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research. In this context, a review of related research is first presented and discussed. The potential offered by such physics-informed deep learning models for computations in geo...
Main Author: | Yared W. Bekele |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-04-01
|
Series: | Journal of Rock Mechanics and Geotechnical Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775520301384 |
Similar Items
-
Modeling a Typical Non-Uniform Deformation of Materials Using Physics-Informed Deep Learning: Applications to Forward and Inverse Problems
by: Yawen Deng, et al.
Published: (2023-04-01) -
Modeling Groundwater Flow in Heterogeneous Porous Media with YAGMod
by: Laura Cattaneo, et al.
Published: (2015-12-01) -
Physics-Informed Neural Networks Integrating Compartmental Model for Analyzing COVID-19 Transmission Dynamics
by: Xiao Ning, et al.
Published: (2023-08-01) -
Deep learning methods for inverse problems
by: Shima Kamyab, et al.
Published: (2022-05-01) -
A Deep Learning Approach for Predicting Two-Dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
by: Yue Lu, et al.
Published: (2022-08-01)