4.5 Review

Review: Theory-guided machine learning applied to hydrogeology-state of the art, opportunities and future challenges

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Article Environmental Sciences

A numerical model of groundwater flow in Karewa-Alluvium aquifers of NW Indian Himalayan Region

Syeedah Raazia et al.

Summary: Process-based groundwater flow models are used to study the hydrogeological characteristics of a complex aquifer system. In this study, a conceptual model and numerical simulation model were developed for the multilayer alluvial aquifer system at the base of Karewa mountain front, providing insights into groundwater recharge and flow dynamics over time. This research contributes to sustainable groundwater development through evaluating groundwater resources and flow patterns.

MODELING EARTH SYSTEMS AND ENVIRONMENT (2022)

Article Engineering, Multidisciplinary

Efficient uncertainty quantification for dynamic subsurface flow with surrogate by Theory-guided Neural Network

Nanzhe Wang et al.

Summary: This study proposes a methodology for efficient uncertainty quantification in dynamic subsurface flow using a surrogate constructed by Theory-guided Neural Network (TgNN), which is specially designed for problems with stochastic parameters. The neural network is trained with available simulation data and theory guidance, and can predict solutions of subsurface flow problems with new stochastic parameters. The TgNN surrogate significantly improves the efficiency of uncertainty quantification tasks compared to simulation based implementation.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method

Yuntian Chen et al.

Summary: Theory-guided hard constraint projection (HCP) integrates domain knowledge with observations, ensuring strict adherence to physical mechanisms in predictions. The model requires only a small amount of labeled data for training and exhibits higher prediction accuracy and robustness compared to fully connected neural networks and soft constraint models. Additionally, theory-guided HCP can accurately predict points outside of the training dataset range due to the incorporation of domain knowledge.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

Article Automation & Control Systems

A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

Navid Zobeiry et al.

Summary: A physics-informed neural network is developed to solve conductive heat transfer PDEs with convective boundary conditions, improving the speed and accuracy of thermal analysis in manufacturing and engineering applications. By using physics-informed activation functions, heat transfer beyond training zone can be accurately predicted, making it a useful tool for real-time evaluation of thermal responses in a wide range of scenarios.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Article Computer Science, Interdisciplinary Applications

Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow

Rui Xu et al.

Summary: In this study, a weak form Theory-guided Neural Network (TgNN-wf) is proposed to improve model accuracy in handling cases with high-order derivatives or strong discontinuities by incorporating the weak form residual of the PDE into the loss function. Experimental results demonstrate the superior performance of TgNN-wf over the strong form TgNN in prediction tasks, especially when strong discontinuity in the parameter or solution space is present.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

Article Water Resources

Machine learning in geo- and environmental sciences: From small to large scale

Pejman Tahmasebi et al.

ADVANCES IN WATER RESOURCES (2020)

Article Multidisciplinary Sciences

Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations

Teeratorn Kadeethum et al.

PLOS ONE (2020)

Article Engineering, Civil

Deep learning of subsurface flow via theory-guided neural network

Nanzhe Wang et al.

JOURNAL OF HYDROLOGY (2020)

Article Multidisciplinary Sciences

A biochemically-interpretable machine learning classifier for microbial GWAS

Erol S. Kavvas et al.

NATURE COMMUNICATIONS (2020)

Article Environmental Sciences

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Phong Tung Nguyen et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)

Article Green & Sustainable Science & Technology

Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

Phong Tung Nguyen et al.

SUSTAINABILITY (2020)

Article Multidisciplinary Sciences

Al Feynman: A physics-inspired method for symbolic regression

Silviu-Marian Udrescu et al.

SCIENCE ADVANCES (2020)

Article Mechanics

Theory -guided machine learning for damage characterization of composites

Navid Zobeiry et al.

COMPOSITE STRUCTURES (2020)

Article Computer Science, Artificial Intelligence

Applying deep learning algorithms to enhance simulations of large-scale groundwater flow in IoTs

Yu-Sen Su et al.

APPLIED SOFT COMPUTING (2020)

Article Geosciences, Multidisciplinary

Physics informed machine learning: Seismic wave equation

Sadegh Karimpouli et al.

GEOSCIENCE FRONTIERS (2020)

Article Computer Science, Interdisciplinary Applications

Obstacle segmentation based on the wave equation and deep learning

Adar Kahana et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2020)

Article Engineering, Multidisciplinary

PPINN: Parareal physics-informed neural network for time-dependent PDEs

Xuhui Meng et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

Article Environmental Sciences

Bootstrap Aggregation and Cross-Validation Methods to Reduce Overfitting in Reservoir Control Policy Search

Zachary P. Brodeur et al.

WATER RESOURCES RESEARCH (2020)

Proceedings Paper Computer Science, Artificial Intelligence

PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly

Nikhil Muralidhar et al.

PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

Arka Daw et al.

PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM) (2020)

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

Article Environmental Sciences

Hydrochemical Analysis and Fuzzy Logic Method for Evaluation of Groundwater Quality in the North Chengdu Plain, China

Adam Khalifa Mohamed et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2019)

Article Computer Science, Artificial Intelligence

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

NATURE MACHINE INTELLIGENCE (2019)

Article Computer Science, Artificial Intelligence

Understanding adversarial training: Increasing local stability of supervised models through robust optimization

Uri Shaham et al.

NEUROCOMPUTING (2018)

Article Environmental Sciences

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Farzaneh Sajedi-Hosseini et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2018)

Article Geosciences, Multidisciplinary

Discovering State-Parameter Mappings in Subsurface Models Using Generative Adversarial Networks

Alexander Y. Sun

GEOPHYSICAL RESEARCH LETTERS (2018)

Article Computer Science, Artificial Intelligence

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

Anuj Karpatne et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)

Article Environmental Sciences

Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

Rahim Barzegar et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2017)

Article Environmental Sciences

Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US

S. Sahoo et al.

WATER RESOURCES RESEARCH (2017)

Article Engineering, Civil

Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

Fi-John Chang et al.

JOURNAL OF HYDROLOGY (2016)

Proceedings Paper Computer Science, Artificial Intelligence

Post Classification Label Refinement Using Implicit Ordering Constraint Among Data Instances

Ankush Khandelwal et al.

2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) (2015)

Article Water Resources

Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization

Evdokia Tapoglou et al.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2014)

Article Engineering, Civil

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Gokmen Tayfur et al.

WATER RESOURCES MANAGEMENT (2014)

Article Computer Science, Interdisciplinary Applications

Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques

Jalal Shiri et al.

COMPUTERS & GEOSCIENCES (2013)

Article Mathematical & Computational Biology

Accounting for linkage disequilibrium in genome-wide association studies: a penalized regression method

Jian Huang et al.

Statistics and Its Interface (2013)

Article Engineering, Civil

A wavelet neural network conjunction model for groundwater level forecasting

Jan Adamowski et al.

JOURNAL OF HYDROLOGY (2011)

Article Chemistry, Physical

Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Geoffroy Hautier et al.

CHEMISTRY OF MATERIALS (2010)

Article Engineering, Environmental

Rule-based fuzzy system for assessing groundwater vulnerability

A. Afshar et al.

JOURNAL OF ENVIRONMENTAL ENGINEERING (2007)

Article Computer Science, Interdisciplinary Applications

Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system

B Tutmez et al.

COMPUTERS & GEOSCIENCES (2006)

Article Environmental Sciences

Applicability of statistical learning algorithms in groundwater quality modeling

A Khalil et al.

WATER RESOURCES RESEARCH (2005)