arXiv subjects

Firas Al-Hindawi

Publications and source records attributed to Firas Al-Hindawi.

3 recordsLinked to original sources

arXiv2026

Open datasets and machine learning for two-phase heat transfer: a review following a spatial-temporal taxonomy

Two-phase heat transfer underpins boiling, condensation, immersion cooling, flow boiling, energy conversion, and electronics thermal management, but its coupled interfacial physics make data reuse and model comparison difficult. This narrative review synthesizes open datasets, machine-learning methods, and reusable software for two-phase heat-transfer research, with emphasis on boiling, multimodal sensing, and thermal-management datasets. We organize the review around a spatial-plus-temporal dimensionality taxonomy, denoted S+TD, that classifies data objects by the dimensionality of the measured, simulated, or derived fields, including 0+0D point values, 0+1D time series, 1+1D profiles, 2+0D images, 2+1D videos, 3+0D/3+1D fields, and mixed multimodal records. The taxonomy is used to connect dataset types to AI tasks such as tabular regression, acoustic sequence learning, image segmentation, video analysis, inverse heat-flux reconstruction, surrogate modeling, and multimodal fusion. The review also develops a roadmap for physics-aware open data, including metadata definitions, evidence and reuse-maturity labels, benchmark splits, decoders, baseline models, and community databanks. NED3 resources are discussed as one implementation case within a broader open-data ecosystem rather than as a complete solution. The main conclusion is that progress in two-phase AI now depends as much on findable, decodable, benchmarkable, and physically interpretable data infrastructure as on model architecture.

cs.LG↗

arXiv2023

Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Domain-knowledge Inspired Pseudo Supervision (DIPS) which utilizes domain-informed Gaussian Mixture Models to generate pseudo annotations to enable the use of traditional supervised metrics. This method was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS proves its effectiveness by outperforming various GAN evaluation metrics, including FID, when selecting the optimal saved checkpoint model. It is also evaluated against truly supervised metrics. Furthermore, DIPS showcases its robustness and interpretability by demonstrating a strong correlation with truly supervised metrics, highlighting its superiority over existing state-of-the-art alternatives. The code and data to replicate the results can be found on the official Github repository: https://github.com/Hindawi91/DIPS

cs.CV↗

arXiv2023

A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation

The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces significantly when tested on data from different domains. To deal with datasets from new domains a model needs to be trained from scratch. Moreover, the dataset needs to be annotated by a domain expert. To address this issue, we propose a new framework to support the generalizability and adaptability of trained CHF detection models in an unsupervised manner. This approach uses an unsupervised Image-to-Image (UI2I) translation model to transform images in the target dataset to look like they were obtained from the same domain the model previously trained on. Unlike other frameworks dealing with domain shift, our framework does not require retraining or fine-tuning of the trained classification model nor does it require synthesized datasets in the training process of either the classification model or the UI2I model. The framework was tested on three boiling datasets from different domains, and we show that the CHF detection model trained on one dataset was able to generalize to the other two previously unseen datasets with high accuracy. Overall, the framework enables CHF detection models to adapt to data generated from different domains without requiring additional annotation effort or retraining of the model.

cs.CV↗