Pages that link to "Item:Q2321952"
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The following pages link to Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines (Q2321952):
Displaying 14 items.
- Prediction-focused subsurface modeling: investigating the need for accuracy in flow-based inverse modeling (Q887627) (← links)
- Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source (Q2001935) (← links)
- Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique (Q2020498) (← links)
- Physics-informed machine learning models for predicting the progress of reactive-mixing (Q2021234) (← links)
- Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning (Q2027161) (← links)
- Data-driven uncertainty quantification in macroscopic traffic flow models (Q2095539) (← links)
- Machine learning and transport simulations for groundwater anomaly detection (Q2186930) (← links)
- Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy (Q2237307) (← links)
- A functional data analysis approach to surrogate modeling in reservoir and geomechanics uncertainty quantification (Q2399815) (← links)
- Uncertainty quantification for porous media flows (Q2506723) (← links)
- The Inverse Heat Transfer Problem of Malan Loess Based on Machine Learning with Finite Element Solver as the Trainer (Q6048282) (← links)
- A surrogate-assisted uncertainty-aware Bayesian validation framework and its application to coupling free flow and porous-medium flow (Q6050704) (← links)
- Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark (Q6074252) (← links)
- Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
