Jose et al. MLST 2025 - Code, Data and Models

DOI

This dataset hosts the code, data and models needed for replication of the work mentioned in Jose et al. MLST 2025. In the paper, an ablation study is conducted to delineate the effects of domain randomisation parameters of synthetically generated training data on the segmentation accuracy. The best model is used to extract high-level statistics from soot filaments in an RQL-type model combustor to enhance the fundamental understanding soot formation, transport and oxidation. B. Jose, K. P. Geigle, F. Hampp, Domain-Randomised Instance-Segmentation Benchmark for Soot in PIV Images, submitted to Machine Learning: Science and Technology (2025)

Python, 3.8

Identifier
DOI https://doi.org/10.18419/DARUS-5512
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5512
Provenance
Creator Jose, Basil ORCID logo; Geigle, Klaus Peter ORCID logo; Hampp, Fabian ORCID logo
Publisher DaRUS
Contributor Jose, Basil; Hampp, Fabian
Publication Year 2025
Funding Reference DFG 456687251
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Jose, Basil (University of Stuttgart); Hampp, Fabian (University of Stuttgart)
Representation
Resource Type Dataset
Format text/x-python; application/octet-stream; application/x-ipynb+json; application/json; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet; text/markdown; application/x-tar
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Version 1.0
Discipline Chemistry; Construction Engineering and Architecture; Engineering; Engineering Sciences; Fluid Mechanics; Heat Energy Technology, Thermal Machines, Fluid Mechanics; Mechanical and industrial Engineering; Mechanics; Mechanics and Constructive Mechanical Engineering; Natural Sciences; Thermal Engineering/Process Engineering