Combining Finite Element and Machine Learning Methods to Predict Structures of Architectured Interlocking Ceramics
Why this work is in the frame
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Bibliographic record
Abstract
Attaining optimum structural ceramic designs calls for an extensive search in a vast design space. Herein, the thermomechanical properties of interlocked ceramics are evaluated and an approach to assist their design under thermal shock loading is proposed. A combination of finite‐element (FE) and machine learning (ML) methods is used to simulate behaviors of systems and then to sweep the vast domain of input combinations to determine the best‐performing designs, respectively. First, FE modeling is done using a limited number of interlocking architectures with different design parameters via Comsol Multiphysics. The simulation data is used for training ML algorithms. Of the examined ML algorithms, Gaussian process regression (GPR), extreme gradient boosting (XGB), and neural networks (NN) more accurately predict the thermomechanical responses of the interlocking ceramics. After validation, the combination of FE and ML approaches is applied to thermal shielding and heat sink applications to find the optimal interlocked ceramics in terms of minimal out‐of‐plane deformation and maximal heat absorption, respectively. The results show the success of the approach in finding optimum designs in a space of more than 2 million cases. The striking success of the ML approach implies its promising potential for predicting physical properties of ceramics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it