polynomial-chaos

Uncertainty Quantification in Climate Modeling

Poster

Efficient Uncertainty Quantification Methodologies for High-Dimensional Climate Land Models

In this report, we proposed, examined and implemented approaches for performing efficient uncertainty quantification (UQ) in climate land models. Specifically, we applied Bayesian compressive sensing framework to a polynomial chaos spectral …

Predictability Assessment in Stochastic Reaction Networks

Talk

Predictability in Stochastic Reaction Networks

Talk

Uncertainty Quantification given Discontinuous Climate Model Response and a Limited Number of Model Runs

Poster

Uncertainty Quantification in Climate Modeling

Talk

Uncertainty Quantification Methodologies for Climate Model Data with Discontinuities

Talk

Advanced Tools for Uncertainty Quantification in Climate Modeling

Talk

Uncertainty Quantification and Data Assimilation - Applications in Stochastic Reaction Networks and Climate Modeling

Talk

Spectral Representation and Reduced Order Modeling of the Dynamics of Stochastic Reaction Networks via Adaptive Data Partitioning

Dynamical analysis tools are well established for deterministic models. However, for many biochemical phenomena in cells the molecule count is low, leading to stochastic behavior that causes deterministic macroscale reaction models to fail. The main …