Dynamic Kolmogorov–Arnold networks for time-varying degradation modeling in solid oxide fuel cells
Bibliographic record
Abstract
Emerging power generation technologies such as solid oxide fuel cells (SOFCs) offer promising pathways for clean and efficient energy conversion. However, their material instability accelerates unexpected degradation, which remains a major barrier to large-scale commercialization. As a practical solution, control and diagnosis systems are integral to optimizing SOFCs’ lifetime and efficiency in real-world operations. Degradation mechanisms induce nonlinear, time-varying patterns in running fuel cells, affecting the effectiveness of control and diagnosis strategies. This study introduces a data-driven machine learning framework for predicting SOFC performance under degradation. Four lab-scale SOFCs were subjected to accelerated degradation tests, generating diverse run-to-failure datasets. To capture the complex, nonstationary dynamics in these data, a dynamic neural network based on Kolmogorov–Arnold approximation theory (DKAN) is developed. DKAN employs univariate splines as learnable activation functions, hierarchically adapting low-dimensional functions to diverse nonlinearities and temporal patterns. Comparative experiments against state-of-the-art sequence models, including LSTM, TCN, WaveNet, DGRU, Informer, and ConvRec, show that DKAN achieves on average 30% lower prediction error (across RMSE, WAPE, and MASE) and 55% faster inference relative to the baselines, while demonstrating superior generalization to unseen degradation patterns. Furthermore, statistical analyses using the Friedman–Nemenyi and ANOVA-Tukey tests confirm the significance of DKAN’s performance improvements across multiple datasets and metrics. These results highlight DKAN’s potential as a lightweight and scalable solution for real-time SOFC diagnostics and control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".