396 citations · 673 across the 27 of their papers we have counts for
7 papers · 1 filter
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
Jacob Roth, David A. Barajas-Solano, Panos Stinis +2
In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stocha…
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
QiZhi He, David Brajas-Solano, Guzel Tartakovsky +1
Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge due to the sparsity of measurements, the heterogeneity of poro…
Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions
Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He
We present a new physics-informed machine learning approach for the inversion of PDE models with heterogeneous parameters. In our approach, the space-dependent partially-observed p…
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling…
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong Ma, Renke Huang, David Barajas-Solano +2
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for…
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
Ramakrishna Tipireddy, David A Barajas-Solano, Alexandre M. Tartakovsky
We use a conditional Karhunen-Loève (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent…
Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients
David A. Barajas-Solano, Alexandre M. Tartakovsky
We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We…