activity
20182020
most citedPhysics-Informed Neural Network Super Resolution for Advection-Diffusion Models

24 citations · 32 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2020

PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data

Wang Zhou, Levente J. Klein, Siyuan Lu

An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework simplifies the de…

cs.CV202024 cited

Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models

Chulin Wang, Eloisa Bentivegna, Wang Zhou +2

Physics-informed neural networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations…

cs.CV20204 cited

Monitoring the Impact of Wildfires on Tree Species with Deep Learning

Wang Zhou, Levente Klein

One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning mod…

cs.LG20194 cited

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning

Xiang Ni, Jing Li, Mo Yu +2

This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize…

cs.LG2018

Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning

Cunxi Yu, Wang Zhou

Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging.…