Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems
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Computer Science > Machine Learning
arXiv:2605.23037 (cs)
[Submitted on 21 May 2026]
Title:Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems
Authors:Christy Dunlap, Hari Pandey, Stephen Pierson, Daniel Curl, Braden Stevens, Mohammad Ishraq Hossain, Annapurna Parjuli, Chinmaya Joshi, Han Hu
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Abstract:Data-driven modeling is becoming central to multiphase transport, electronics cooling, acoustic diagnostics, and thermal-fluid digital twins, but progress is limited by fragmented datasets and raw instrument files that are difficult to decode, reuse, or benchmark. This paper presents an open ecosystem of multimodal datasets and open-source software packages developed by the Nano Energy and Data-Driven Discovery (NED3) Laboratory for reproducible AI-enabled thermal-fluid research. We introduce a spatial-plus-temporal dimensionality framework, denoted S+TD, to classify datasets by the dimensionality of measured or simulated fields, including 0+0D point values, 0+1D time series, 1+0D profiles, 2+0D images, 2+1D videos, 3+0D volumetric fields, and multimodal combinations. We organize public NED3 datasets spanning boiling images, acoustic and thermal measurements, high-speed videos, infrared thermography, thermal-resistance measurements, CFD-generated fields, design files, and acoustic-emission data. We also describe complementary software packages, including BubbleID, SeqReg, CFDTwin, IRISApp, decode-wfs, AELab, and FlowLab, which support computer vision, sequence regression, surrogate modeling, infrared analysis, waveform decoding, acoustic-emission analysis, and multimodal diagnostics. Particular emphasis is placed on SeqReg, a general sequence-regression library for 0+1D, 1+1D, and 2+1D data, with applications such as nonintrusive heat-flux estimation. Finally, we discuss future community efforts to build interoperable thermal-fluid databanks and curated AI/ML tool libraries that connect datasets, metadata, decoders, baselines, benchmarks, and physically interpretable models.
| Comments: | 23 pages, 7 figures |
| Subjects: | Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2605.23037 [cs.LG] |
| (or arXiv:2605.23037v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23037
arXiv-issued DOI via DataCite (pending registration)
|
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View a PDF of the paper titled Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems, by Christy Dunlap and 8 other authors
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