3D Multi-Physics Uncertainty Quantification using Physics-Based Machine Learning

3D multi-physics uncertainty quantification using physics-based machine learning

Benefits of Global Sensitivity Analysis and Reduced Order Modeling for Basin-Scale Process Simulations

Certified Reduced Basis Method in Geosciences Addressing the challenge of high dimensional problems

preprint

Certified reduced basis method in geosciences

scholarly article

Crustal-Scale Thermal Models: Revisiting the Influence of Deep Boundary Conditions

preprint

Crustal-Scale Thermal Models: Revisiting the Influence of Deep Boundary Conditions

Crustal-scale thermal models: revisiting the influence of deep boundary conditions

scholarly article

Effects of Transient Processes for Thermal Simulations of the Central European Basin

Effects of transient processes for thermal simulations of the Central European Basin

scientific article published in 2021

Global Sensitivity Analysis to Optimize Basin-Scale Conductive Model Calibration – A Case Study From the Upper Rhine Graben

preprint

Global sensitivity analysis to optimize basin-scale conductive model calibration – A case study from the Upper Rhine Graben

How biased are our models? – A Case Study of the Alpine Region

preprint

How biased are our models? – a case study of the alpine region

scholarly article

How well do we know our models?

Making sense of Isogeometric Analysis for geothermal applications: Parametric geomodelling (NURBS) for fast model construction, simulation and adaptation

Perspectives of Physics-Based Machine Learning for Geoscientific Applications Governed by Partial Differential Equations

Physics-based machine learning for modeling thermo-hydraulic processes in a coaxial deep borehole heat exchanger, considering an explicit reservoir-wellbore representation: A case study of Cornwall, UK  

Productivity enhancement of geothermal wells through fault zones: Efficient numerical evaluation of a parameter space for the Upper Jurassic aquifer of the North Alpine Foreland Basin

Quantifying Geodynamical Influences through Physics-Based Machine Learning: A Case Study from the Alpine Region 

The Application of Neural Operator in subsurface process simulation 

The Value of Scientific Machine Learning for Geothermal Applications

Uncertainty Quantification for Basin-Scale Conductive Models

Uncertainty Quantification for Basin-Scale Geothermal Conduction Models

Uncertainty quantification for basin-scale geothermal conduction models

scientific article published on 10 March 2022

Uncertainty quantification with a physics-based machine learning method for geothermal-well targeting: A case study of The Hague, Netherlands