AI-accelerated physics-informed transient real-time digital-twin of SMR-based multi-domain submarine power distribution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 | 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 it