📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Analyzing drop coalescence in microfluidic device with a deep learning generative model (2023)

First Author: Zhu K

Attributed to: PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) funded by EPSRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.48550/arxiv.2305.00261

Publication URI: https://arxiv.org/abs/2305.00261

Type: Other