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SeqReg
UARK-NED3/SeqReg
SeqReg is a machine learning model from UARK-NED3. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
SeqReg is an open sequence-regression software package for experimental pool-boiling heat-flux prediction using hydrophone, acoustic-emission (AE) hit, and optical-image inputs. The canonical software source, installa…
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Updated Sep 14, 2026
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.joblib567 MB · 50%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Sep 14, 2026
- Updated
- Sep 14, 2026
- SHA
- de972e20
Model card
From the Hugging Face model README
SeqReg pretrained models
SeqReg is an open sequence-regression software package for experimental pool-boiling heat-flux prediction using hydrophone, acoustic-emission (AE) hit, and optical-image inputs. The canonical software source, installation instructions, tutorials, and implementation history are maintained at cldunlap73/SeqReg.
This repository provides the pretrained artifacts released with SeqReg. It is not a hosted inference endpoint.
Included artifacts
| File | Intended input and role | SHA-256 |
|---|---|---|
HydReg.joblib | Hydrophone sequence regression; the canonical documentation specifies FFT preprocessing and SeqLen=4000. | FBEB30D598EC7BAB58F0DD981BDB3A7C2149FC2EACC04E0E74DF60DAAFA4DAF8 |
Hit2Flux_weights.h5 | AE-hit sequence regression; the canonical documentation specifies FFT preprocessing, SeqLen=25, and sequence output. | 9942A030D9399D2D6F4AFA9A163B95B0112855EF78CE5FC99BD3975B9629A86D |
ImgReg.hdf5 | Optical-image sequence-regression artifact. | 198254381899CEC72BA7366ECC4BEE3AD1C67D5E32D15F281DF23D90287C747F |
ImagePCA.pkl | PCA artifact required by ImgReg; canonical configuration specifies PCAnpy, pcskeep=40, and SeqLen=200. | B565E08A80B8F85ED834D485AC295FBCE5876EE5BC1C46269997A29112DB4C0D |
Data and reproducibility
The self-contained model-and-test-data package is available at UARK-NED3/BoilingBench-SeqReg. The original public SeqReg OSF project remains the release record referenced by the software documentation.
Use the original SeqReg README for required dependencies, data layout, loading logic, and tutorials. The Hugging Face Dataset retains the supplied test-data directory structure; do not infer a model-input convention from filenames alone.
Validation status and limitations
The four local artifacts were SHA-256 verified before upload. They were not loaded or re-evaluated in this release workflow. These models were developed for the documented pool-boiling measurement modalities and configurations; evaluate transfer to another facility, fluid, sensor chain, surface, operating regime, or transient protocol before research use.
joblib and pickle artifacts can execute code when deserialized. Load only files obtained from this verified repository and in a controlled environment.
Attribution and licensing
SeqReg and the linked OSF project are released under Apache-2.0. Cite the applicable publication and use the canonical repository for software issues and updates.
- C. Dunlap, H. Pandey, E. Weems, and H. Hu, Nonintrusive Heat Flux Quantification Using Acoustic Emissions During Pool Boiling, Applied Thermal Engineering, 2023. https://doi.org/10.1016/j.applthermaleng.2023.120558
- C. Dunlap, C. Li, H. Pandey, and H. Hu, Hit2Flux: A Machine Learning Framework for Boiling Heat Flux Prediction Using Hit-Based Acoustic Emission Sensing, AI in Thermal Fluids, 2025. https://doi.org/10.1016/j.aitf.2025.100002
- C. Dunlap, C. Li, H. Pandey, Y. Sun, and H. Hu, A Temporal-Spatial Framework for Efficient Heat Flux Monitoring of Transient Boiling, IEEE Transactions on Instrumentation and Measurement, 2024. https://doi.org/10.1109/TIM.2024.3460944
- License
- apache-2.0
- Created
- Sep 14, 2026
- Updated
- Sep 14, 2026