We propose a methodology for improving the accuracy of surrogate models ...
We present a model inversion algorithm, CKLEMAP, for data assimilation a...
In this paper, we develop a physics-informed neural network (PINN) model...
We develop a physics-informed machine learning approach for large-scale ...
Redox flow batteries (RFBs) offer the capability to store large amounts ...
We extend stochastic basis adaptation and spatial domain decomposition
m...
Real-time state estimation and forecasting is critical for efficient
ope...
Time series forecasting remains a central challenge problem in almost al...
We propose a novel approach to model viscoelasticity materials using neu...
Data assimilation for parameter and state estimation in subsurface trans...
We propose a new forecasting method for predicting load demand and gener...
We present a new approach for constructing a data-driven surrogate model...
We use a conditional Karhunen-Loève (KL) model to quantify and reduce
un...
We present two approximate Bayesian inference methods for parameter
esti...
We present a multivariate Gaussian process regression approach for param...
Generative Adversarial Networks (GANs) are becoming popular choices for
...