dimensionality-reduction

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of …

Hit Twice by the Curse of Dimensionality; Spatio-Temporal Land Model Calibration using Karhunen-Loeve and Sparse Polynomial Chaos Expansions

Poster

Dimensionality Reduction and Physics-Informed Neural Networks for Climate Land Models

Talk

Dimensionality Reduction and Physics-Informed Recurrent Neural Networks for Climate Land Models

Poster

Compressive Sensing Adaptation for Polynomial Chaos Expansions

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present …

Parametric Uncertainty Quantification and Dimensionality Reduction for ALM at FLUXNET sites

Poster

Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing

Uncertainty quantification in complex physical models is often challenged by the computational expense of these models. One often needs to operate under the assumption of sparsely available model simulations. This issue is even more critical when …

Bayesian Compressive Sensing and Dimensionality Reduction for High-Dimensional Models

Talk

Adaptive Basis Selection and Dimensionality Reduction with Bayesian Compressive Sensing

Talk