466 citations · 1.7k across the 54 of their papers we have counts for
12 papers · 1 filter
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…
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
Jing Li, Alexandre M Tartakovsky
We present a new approach for constructing a data-driven surrogate model and using it for Bayesian parameter estimation in partial differential equation (PDE) models. We first use…
A New Approach For Learning Coarse-Grained Potentials with Application to Immiscible Fluids
Peiyuan Gao, Xiu Yang, Alexandre M. Tartakovsky
Even though atomistic and coarse-grained (CG) models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are…