activity
20182021
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.1k across the 2 of their papers we have counts for

collaborators

5 papers

eess.IV2021

Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces

Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban +5

Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superio…

cs.LG20212 cited

Progressive Neural Image Compression with Nested Quantization and Latent Ordering

Yadong Lu, Yinhao Zhu, Yang Yang +2

We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstrea…

physics.comp-ph20191.1k cited

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1

Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…

cs.CV2018

A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images

Yide Zhang, Yinhao Zhu, Evan Nichols +4

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography,…

stat.ML2018

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras +2

Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multip…