Yinhao Zhu

TL;DR: This paper provides a methodology that incorporates the governing equations of the physical model in the loss/likelihood functions of the model predictive density and the reference conditional density as a minimization problem of the reverse Kullback-Leibler (KL) divergence.

TL;DR: This approach achieves state of the art performance in terms of predictive accuracy and uncertainty quantification in comparison to other approaches in Bayesian neural networks as well as techniques that include Gaussian processes and ensemble methods even when the training data size is relatively small.

TL;DR: In this paper, a deep convolutional encoder-decoder neural network was proposed to characterize the high-dimensional time-dependent outputs of the dynamic multi-phase flow model with a 2500-dimensional stochastic permeability field.

TL;DR: This paper constructs a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising and uses this dataset to benchmark 10 representative denoised algorithms and finds that deep learning methods have the best performance.

TL;DR: A deep convolutional encoder‐decoder neural network methodology is proposed to tackle surrogate modeling problems in dynamic multiphase flow problems and is capable of accurately characterizing the spatiotemporal evolution of the pressure and discontinuous CO2 saturation fields.