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Related identifier:
Creator/Author:
Dreisbach, Maximilian https://orcid.org/0000-0001-6308-0982 [Dreisbach, Maximilian]

Kiyani, Elham [Kiyani, Elham]

Kriegseis, Jochen https://orcid.org/0000-0002-2737-2539 [Kriegseis, Jochen]

Karniadakis, George Em [Karniadakis, George Em]

Stroh, Alexander https://orcid.org/0000-0003-0850-9883 [Stroh, Alexander]
Contributors:
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Title:
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction (Research Data)
Additional titles:
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Description:
(Abstract) Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conventional optical diagnostics, including shadowgraphy and particle image velocimetry, provide valuable insights but are lim...

(Technical Remarks) This dataset consists of raw and processed images, supplementary videos, and the neural network weights of the trained PINNs from the research work "PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction". The images were obtained by glare-point shadowgraph...
Keywords:
two-phase flow
interfacial dynamics
physics-informed neural networks
volumetric reconstruction
deep learning
Related information:
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Language:
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Version:
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Production year:
Subject areas:
Engineering
Resource type:
Dataset
Data source:
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Software used:
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Data processing:
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Publication year:
Rights holders:

Kiyani, Elham

Karniadakis, George Em
Funding:
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Name Storage Metadata Upload Action
Status:
Published
Uploaded by:
kitopen
Created on:
Archiving date:
2026-03-16
Archive size:
12.1 GB
Archive creator:
kitopen
Archive checksum:
48645a9bbd1735bdea458b6a01a102c4 (MD5)
Embargo period:
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