Can Graph Neural Networks Help Identify Promising Thermal Management System Architectures Among Vast Numbers of Possibilities?
Bibliographic record
Abstract
Abstract The more efficient a thermal management system must be across a diverse range of conditions, the greater the intricacy of its design is required. Yet, the computational bottlenecks of enumerating and analyzing potential system architectures often render the best solutions impractical. This study evaluates the feasibility of accelerating system architecture performance evaluation by leveraging graph neural network (GNN)-based regression as a cost-effective alternative to traditional open-loop optimal control (OLOC) analysis. We examine a case study where enumerating all possible system architectures is computationally feasible, but directly analyzing the performance of each is not. Instead, we analyze a small subset of architectures and use these data to train a generalizable surrogate model capable of evaluating the remaining architectures within the available computational budget. After the training, the predicted performance values are sorted to obtain the estimated best configurations. Then, there are two options: (1) select the highest-ranked configuration as the optimal solution or (2) choose a small subset of test data with the highest estimated ranks and evaluate them using OLOC to get a more accurate result for the correct optimal configuration. Our results show that training a GNN with approximately 30% of the architectures was sufficient to predict the performance of the remaining 70% with an average mean squared error (MSE) loss of 0.6, achieving a 92% reduction in computational cost overall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".