Bayesian probabilistic machine learning analysis of ceramic‐coated ultra‐high‐temperature carbon/carbon composites
Bibliographic record
Abstract
Abstract Regulatory agencies and key stakeholders are increasingly promoting the use of probabilistic approaches in design processes for large corporations. This shift is particularly emphasized in analyzing mechanical properties, such as fatigue and failure prediction. Additionally, the use of probabilistic artificial intelligence represents a transformative advancement in material science that leads to enhanced predictive accuracy and robust decision‐making capabilities. These artificial intelligence methods enable more informed decision‐making in the design and evaluation of advanced materials by quantifying uncertainty and offering probabilistic assessments, particularly for applications involving extreme environments. High‐temperature materials, such as carbon/carbon (C/C) composites, are essential for modern technological applications. However, their vulnerability to oxidation poses a significant barrier, indicating the necessity for effective protective coatings. The application of these coatings to C/C composites is complex and has hindered their widespread use in high‐temperature settings. In this study, we utilize finite element analysis (FEA) and machine learning (ML) combined with Bayesian probability to examine the behavior of silicon carbide ceramic‐coated cubic C/C composites. The investigation focuses on how stress and strain evolve under varying thermal conditions and cyclic thermal loading from a probabilistic perspective. This work integrates FEA and Bayesian probabilistic‐based ML to enhance the predictive power for evaluating ultra‐high‐temperature materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".