AI-accelerated physics-informed transient real-time digital-twin of SMR-based multi-domain submarine power distribution
Bibliographic record
Abstract
Small Modular Reactors (SMRs) have emerged as promising solutions for next-generation marine propulsion systems due to their enhanced efficiency, reduced maintenance requirements, and extended operational capabilities. However, traditional transient modeling methods for these systems often rely on conventional numerical integration techniques, which encounter significant challenges when dealing with nonlinear system dynamics, leading to considerable computational latency and extensive parameter tuning efforts. To address these limitations, this paper introduces an artificial intelligence (AI)-accelerated physics-informed real-time digital-twin (RTDT) for an SMR-based multi-domain submarine power distribution system. The proposed approach integrates physics-informed machine learning (PIML) methodologies, combining neural network models with explicit physical constraints. Leveraging the parallel computing capabilities of the Xilinx® UltraScale+ FPGA hardware platform, the proposed framework significantly reduces computational latency. The emulation results validate the effectiveness and efficiency of the proposed PIML-based RTDT, achieving mean percentage absolute errors (MPAEs) consistently below 1%, thus demonstrating superior performance compared to classical numerical methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".