research
∙
07/05/2023
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
We propose a methodology for improving the accuracy of surrogate models ...
share
research
∙
10/05/2022
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Conformal prediction is a widely used method to quantify uncertainty in ...
research
∙
06/16/2021
Lorenz System State Stability Identification using Neural Networks
Nonlinear dynamical systems such as Lorenz63 equations are known to be c...
research
∙
03/04/2021
Time-dependent stochastic basis adaptation for uncertainty quantification
We extend stochastic basis adaptation and spatial domain decomposition m...
research
∙
10/15/2020
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework
Partial differential equations (PDEs) are fundamental for theoretically ...
research
∙
10/09/2020
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Real-time state estimation and forecasting is critical for efficient ope...
research
∙
09/16/2020
Bayesian phase estimation with adaptive grid refinement
We introduce a novel Bayesian phase estimation technique based on adapti...
research
∙
04/09/2020
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation
We present FPDetect, a low overhead approach for detecting logical error...
research
∙
04/09/2020
Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation
We present FPDetect, a low overhead approach for detecting logical error...
research
∙
10/09/2019
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
We propose a new forecasting method for predicting load demand and gener...
research
∙
04/17/2019
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
We use a conditional Karhunen-Loève (KL) model to quantify and reduce un...
research
∙
04/02/2019