most citedPrediction of the morphological evolution of a splashing drop using an encoder-decoder

7 citations · 8 across the 4 of their papers we have counts for

collaborators

4 papers

physics.flu-dyn2023

The effects of cavitation position on the velocity of a laser-induced microjet extracted using explainable artificial intelligence

Daichi Igarashi, Jingzu Yee, Yuto Yokoyama +2

The control of the velocity of a high-speed laser-induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity i…

physics.flu-dyn20231 cited

Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence

Jingzu Yee, Daichi Igarashi, Pradipto +2

This study reveals a possible correlation between splashing morphology and the normalized impact force exerted by an impacting drop on a solid surface. This finding is obtained fro…

physics.flu-dyn20237 cited

Prediction of the morphological evolution of a splashing drop using an encoder-decoder

Jingzu Yee, Daichi Igarashi, Shun Miyatake +1

The impact of a drop on a solid surface is an important phenomenon that has various implications and applications. However, the multiphase nature of this phenomenon causes complica…

physics.flu-dyn2022

Features of a Splashing Drop on a Solid Surface and the Temporal Evolution extracted through Image-Sequence Classification using an Interpretable Feedforward Neural Network

Jingzu Yee, Daichi Igarashi, Akinori Yamanaka +1

This paper reports the features of a splashing drop on a solid surface and the temporal evolution, which are extracted through image-sequence classification using a highly interpre…