Physics-informed machine learning for fault-leakage reduced-order modeling

Meguerdijian, Saro; Pawar, Rajesh J.; Chen, Bailian; Gable, Carl W.; Miller, Terry A.; Jha, Birendra

doi:10.1016/j.ijggc.2023.103873

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Other1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

Mao,

Chen,

Malki

et al. 2024

Applied Energy

View full text Add to dashboard Cite

Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

Mao,

Chen,

Malki

et al. 2024

Applied Energy

View full text Add to dashboard Cite

A review of intelligent decision-making strategy for geological CO2 storage: Insights from reservoir engineering

Mao,

Jahanbani Ghahfarokhi

2024

Geoenergy Science and Engineering

View full text Add to dashboard Cite

A review and benchmark of feature importance methods for neural networks

Mandler,

Weigand

2024

ACM Comput. Surv.

View full text Add to dashboard Cite

Feature attribution methods (AMs) are a simple means to provide explanations for the predictions of black-box models like neural networks. Due to their conceptual differences, the numerous different methods, however, yield ambiguous explanations. While this allows for obtaining different insights into the model, it also complicates the decision which method to adopt. This paper, therefore, summarizes the current state of the art regarding AMs, which includes the requirements and desiderata of the methods themselves as well as the properties of their explanations. Based on a survey of existing methods, a representative subset consisting of the δ -sensitivity index, permutation feature importance, variance-based feature importance in artificial neural networks and DeepSHAP, is described in greater detail and, for the first time, benchmarked in a regression context. Specifically for this purpose, a new verification strategy for model-specific AMs is proposed. As expected, the explanations’ agreement with the intuition and among each other clearly depends on the AMs’ properties. This has two implications: First, careful reasoning about the selection of an AM is required. Secondly, it is recommended to apply multiple AMs and combine their insights in order to reduce the model’s opacity even further.

show abstract

Physics-informed machine learning for fault-leakage reduced-order modeling

Cited by 5 publications

References 39 publications

Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

A review of intelligent decision-making strategy for geological CO2 storage: Insights from reservoir engineering

A review and benchmark of feature importance methods for neural networks

Contact Info

Product

Resources

About