Predicting Wave Propagation for Varying Bathymetry Using Conditional Convolutional Autoencoder Network
Bibliographic record
Abstract
Abstract In marine engineering applications, a crucial demand exists for the accurate and dependable prediction of far-field noise emanating from marine vessels. Traditional full-order models relying on the Navier-Stokes equations prove impractical, and advanced model reduction techniques can be inefficient for reliable far-field noise prediction. Recent advancements in deep learning-based reduced-order models have demonstrated effectiveness, achieving speeds several orders of magnitude faster than full-order simulations through the utilization of convolutional neural network architectures. Despite their effectiveness, existing models encounter considerable difficulties when forecasting wave propagation over extended time horizons and making predictions for distant locations. This research endeavors to enhance the predictive capacity of underwater radiated noise in far-field scenarios by refining the network architecture and integrating bathymetry information into the neural network input. Reduced-order models utilizing deep learning often rely on auto-regressive prediction, lacking information about far-field bathymetry. To overcome this limitation, we introduce a new range-conditional convolutional autoencoder network, which incorporates ocean bathymetry data into the input. To showcase the efficacy of our range-conditional convolutional autoencoder network, we examine a benchmark scenario involving far-field prediction over Dickin’s seamount. Our proposed architecture adeptly captures the transmission loss over a range-dependent, varying bathymetric profile. The architecture can be integrated into an adaptive management system for underwater radiated noise while providing real-time end-to-end mapping between near-field ship noise sources and marine mammals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".