Search Results for author:
Found 6 papers, 1 papers with code
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 of the observable response of physical systems as a function of the systems' spatially heterogeneous parameter fields with applications to uncertainty quantification and parameter estimation in high-dimensional problems.
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 the uncertainty of a classifier under the assumption of exchangeability (e. g., IID data).
Lorenz System State Stability Identification using Neural Networks
We then test the ability of the neural network models to identify the stable and unstable states on a different Lorenz system that is generated using different initial conditions.
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR, with both forecasts being significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast.
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
We propose a new forecasting method for predicting load demand and generation scheduling.
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
We investigate the use of discrete and continuous versions of physics-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system.
