We propose a new class of Bayesian neural networks (BNNs) that can be trained using
noisy data of variable fidelity, and we apply them to learn function approximations as well as …

Optimization and uncertainty quantification have been playing an increasingly important role
in computational hemodynamics. However, existing methods based on principled modeling …

We propose a novel use of generative adversarial networks (GANs)(i) to make predictions in
time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we …

Traditional methods for uncertainty quantification (UQ) struggle with the curse of
dimensionality when dealing with high-dimensional problems. One approach to address this …

Uncertainty in data is certainly one of the main problems in epidemiology, as shown by the
recent COVID-19 pandemic. The need for efficient methods capable of quantifying …

We analyze the regression accuracy of convolutional neural networks assembled from
encoders, decoders and skip connections and trained with multifidelity data. Besides …

We propose the novel use of a generative adversarial network (GAN)(i) to make predictions
in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we …

To reduce training costs, several Deep neural networks (DNNs) that can learn from a small
set of HF data and a sufficient number of low-fidelity (LF) data have been proposed. In these …

Scientific and engineering problems often involve parametric partial differential equations
(PDEs), such as uncertainty quantification, optimizations, and inverse problems. However …

Model Order Reduction is an approximation of the main system so that the simplified system
retains important features of the main system. Deep learning technology is a recent …