- Uncertainty quantification in stochastic inversion with dimensionality ...🔍
- Data Assimilation🔍
- Integrating deep learning and discrete cosine transform for surface ...🔍
- Uncertainty Quantification and Probabilistic Modeling🔍
- Deep generative inversion of ERT data for electrical resistivity🔍
- Federated Conformal Predictors for Distributed Uncertainty ...🔍
- Forecasting Geomagnetic Storm Disturbances and Their ...🔍
- Efficient Inversion and Uncertainty Quantification of a Tephra Fallout ...🔍
Data Space Inversion for Efficient Predictions and Uncertainty ...
Uncertainty quantification in stochastic inversion with dimensionality ...
The model and data dimension reduc- tion make the computation efficient for large-scale data assimilation problems. Figure 19 shows the predicted production ...
... uncertainties of decision-critical predictions.) Specifications of ... Efficient SVD-assisted inversion using “super parameters”;; Jacobian matrix ...
Integrating deep learning and discrete cosine transform for surface ...
... efficiently estimate the inversion uncertainties. Our neural ... uncertainties from the data to the model space. We first test the ...
Uncertainty Quantification and Probabilistic Modeling - USACM
... predictions for when no data is available. In stacking networks, we ... Abstract: Remote sensing data sets produced by NASA and other space agencies ...
Deep generative inversion of ERT data for electrical resistivity
Moreover, a single solution does not yield enough information for accurate uncertainty assessment. Contrary to deterministic approaches, stochastic inversion ...
Federated Conformal Predictors for Distributed Uncertainty ...
... predictions instead of outputting a prediction for every data point. In ... y Arcas, B. A. Communication-efficient learning of deep networks from decentralized ...
Forecasting Geomagnetic Storm Disturbances and Their ...
The bands in Figure 12 correspond to the PICP calculated after including a 4.22 ± 1.43 nT uncertainty on the predicted SYM-H data. ... NASA space physics data ...
Efficient Inversion and Uncertainty Quantification of a Tephra Fallout ...
parameter space for best fit solutions to the data. Because inversion ideally provides a robust estimate of uncertainty in parameters such as eruption mass ...
UiS Brage: Reservoir Optimization through Data-Space Inversion
These direct forecasting techniques have shown enormous potential, and data space inversion (DSI) is one such technique. DSI conditions its ...
History Matching for Geological Carbon Storage Using Data-Space ...
In data-space inversion (DSI), history-matched quantities of interest, e.g., posterior pressure and saturation fields conditioned to ...
Seminar for machine learning and UQ in scientific computing
... prediction of hemodynamics efficiently with small real-world data sets. I will touch upon several of our recent works in scale invariant and rotation ...
History Matching for Geological Carbon Storage using Data-Space ...
Substantial uncertainty reduction in posterior pressure and saturation fields is achieved in all cases. The framework is applied to efficiently ...
Accelerating Ensemble-Based Well Control Optimization with ES ...
Recent developments in direct forecasting techniques such as data-space inversion (DSI) have shown promising results to alleviate the ...
Data-Space Inversion for Rapid Physics-Informed Direct Forecasting ...
We propose a physics-informed unconventional forecasting (PIUF) framework that combines simulations and data analytics for robust field applications.
Land Subsidence Model Inversion with the Estimation of Both Model ...
The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both ...
Quantifying predictive uncertainty in damage classification for ...
The augmented data from the fine-tuned autoencoder is further applied for ML-based defect size classification. This study conducted prediction ...
A Guide to Using PEST for Model-Parameter and Predictive ...
Regularized Inversion and Uncertainty ... prediction uncertainty with or without specified observation data. Be- cause ...
On the Implicit Bias of Predicting in Latent Space ... A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models ...
RTO-TKO1.pdf - Marine EM Laboratory
We present a method for computing a meaningful uncertainty quantification (UQ) for reg- ularized inversion of electromagnetic (EM) geophysical ...
Invertible neural networks for uncertainty quantification in refraction ...
Our findings reveal that this INN-based workflow can perform tomographic inversion while integrating an implicit prior in the form of a set of ...