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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

On the feasibility of using physics-informed machine learning for underground reservoir pressure management
D. R. Harp, Dan O’Malley, Bicheng Yan, et al.
Expert Systems with Applications (2021) Vol. 178, pp. 115006-115006
Open Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

A robust deep learning workflow to predict multiphase flow behavior during geological CO2 sequestration injection and Post-Injection periods
Bicheng Yan, Bailian Chen, D. R. Harp, et al.
Journal of Hydrology (2022) Vol. 607, pp. 127542-127542
Open Access | Times Cited: 73
A gradient-based deep neural network model for simulating multiphase flow in porous media
Bicheng Yan, D. R. Harp, Bailian Chen, et al.
Journal of Computational Physics (2022) Vol. 463, pp. 111277-111277
Open Access | Times Cited: 69
A critical review of physics-informed machine learning applications in subsurface energy systems
Abdeldjalil Latrach, Mohamed Lamine Malki, Misael M. Morales, et al.
Geoenergy Science and Engineering (2024) Vol. 239, pp. 212938-212938
Open Access | Times Cited: 18
A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media
Bicheng Yan, D. R. Harp, Bailian Chen, et al.
Fuel (2021) Vol. 313, pp. 122693-122693
Open Access | Times Cited: 70
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
Danial Amini, Ehsan Haghighat, Rubén Juanes
Journal of Computational Physics (2023) Vol. 490, pp. 112323-112323
Open Access | Times Cited: 28
Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration
Zeeshan Tariq, Ertugrul Umut Yildirim, Manojkumar Gudala, et al.
Fuel (2023) Vol. 341, pp. 127677-127677
Closed Access | Times Cited: 22
Physics-informed machine learning for reservoir management of enhanced geothermal systems
Bicheng Yan, Zhen Xu, Manojkumar Gudala, et al.
Geoenergy Science and Engineering (2024) Vol. 234, pp. 212663-212663
Closed Access | Times Cited: 8
A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning
Hao Wu, Nicholas Lubbers, Hari Viswanathan, et al.
Applied Energy (2021) Vol. 287, pp. 116580-116580
Open Access | Times Cited: 41
Physics-informed graph neural network for spatial-temporal production forecasting
Wendi Liu, Michael J. Pyrcz
Geoenergy Science and Engineering (2023) Vol. 223, pp. 211486-211486
Open Access | Times Cited: 20
Computational applications using data driven modeling in process Systems: A review
Sumit K. Bishnu, Sabla Y. Alnouri, Dhabia M. Al-Mohannadi
Digital Chemical Engineering (2023) Vol. 8, pp. 100111-100111
Open Access | Times Cited: 18
Prediction of Pure Mineral-H2-Brine Wettability Using Data-Driven Machine Learning Modeling: Implications for H2 Geo-Storage
Muhammad Ali, Zeeshan Tariq, Muhammad Mubashir, et al.
Day 3 Wed, February 23, 2022 (2024)
Closed Access | Times Cited: 6
Reservoir Modeling and Optimization Based on Deep Learning with Application to Enhanced Geothermal Systems
Bicheng Yan, Zhen Xu, Manojkumar Gudala, et al.
SPE Reservoir Characterisation and Simulation Conference and Exhibition (2023)
Open Access | Times Cited: 14
A review on full-, zero-, and partial-knowledge based predictive models for industrial applications
Stefano Zampini, Guido Parodi, Luca Oneto, et al.
Information Fusion (2025), pp. 102996-102996
Open Access
Multi-scale enhanced multiwavelet-based operator learning model for multiphase flow simulation
Yunlong Dong, Tao Song, Xue Li, et al.
Physics of Fluids (2025) Vol. 37, Iss. 3
Open Access
A Critical Evaluation of Using Physics-Informed Neural Networks for Simulating Voltammetry: Strengths, Weaknesses and Best Practices
Haotian Chen, Christopher Batchelor‐McAuley, Enno Kätelhön, et al.
Journal of Electroanalytical Chemistry (2022) Vol. 925, pp. 116918-116918
Open Access | Times Cited: 21
Data-Driven Machine Learning Modeling of Mineral/CO2/Brine Wettability Prediction: Implications for CO2 Geo-Storage
Zeeshan Tariq, Muhammad Ali, Bicheng Yan, et al.
(2023)
Closed Access | Times Cited: 12
Estimation of heterogeneous permeability using pressure derivative data through an inversion neural network inspired by the Fast Marching Method
Bicheng Yan, Chen Li, Zeeshan Tariq, et al.
Geoenergy Science and Engineering (2023) Vol. 228, pp. 211982-211982
Closed Access | Times Cited: 11
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
Aleksandra Pachalieva, Daniel O’Malley, D. R. Harp, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 18
A combination of large eddy simulation and physics-informed machine learning to predict pore-scale flow behaviours in fibrous porous media: A case study of transient flow passing through a surgical mask
Mehrdad Mesgarpour, Rabeeah Habib, Mostafa Safdari Shadloo, et al.
Engineering Analysis with Boundary Elements (2023) Vol. 149, pp. 52-70
Open Access | Times Cited: 10
Physics-informed machine learning for noniterative optimization in geothermal energy recovery
Bicheng Yan, Manojkumar Gudala, Hussein Hoteit, et al.
Applied Energy (2024) Vol. 365, pp. 123179-123179
Closed Access | Times Cited: 3
Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure
Seyed Poorya Mirfallah Lialestani, David Parcerisa Duocastella, Mahjoub Himi, et al.
Energies (2022) Vol. 15, Iss. 13, pp. 4602-4602
Open Access | Times Cited: 14
Improving deep learning performance for predicting large-scale geological $${{CO}_{2}}$$ sequestration modeling through feature coarsening
Bicheng Yan, D. R. Harp, Bailian Chen, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 14
Data-Driven Geothermal Reservoir Modeling: Estimating Permeability Distributions by Machine Learning
Anna Suzuki, Ken–ichi Fukui, Shinya Onodera, et al.
Geosciences (2022) Vol. 12, Iss. 3, pp. 130-130
Open Access | Times Cited: 10
On the feasibility of an ensemble multi-fidelity neural network for fast data assimilation for subsurface flow in porous media
Yating Wang, Bicheng Yan
Expert Systems with Applications (2024), pp. 125774-125774
Closed Access | Times Cited: 1
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems
Abdeldjalil Latrach, Mohamed Lamine Malki, Misael M. Morales, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 4
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