Data-Driven Differentiable Simulations for Three-Dimensional Range-Dependent Underwater Acoustics
Bibliographic record
Abstract
Abstract Artificial intelligence is transforming the shipping industry by providing cutting-edge solutions that improve ship efficiency and safety. The combination of these technologies, as well as the unparalleled power of sensor technologies and data analysis, has the potential to transform shipping by effectively processing real-time data, identifying potential hazards, reducing the impact on marine life and assisting in decision making. There is a critical need for accurate and reliable prediction of far-field noise emanating from shipping vessels. Traditional full-order models relying on high-dimensional PDEs can be inefficient for real-time far-field noise prediction. Recent advancements in deep learning-based reduced-order models have demonstrated speeds several orders of magnitude faster than full-order simulations. However, existing models lack uncertainty quantification for decision-making and safety-critical tasks. This research addresses this gap by focusing on quantifying uncertainty in data-driven models for underwater acoustics. Using stochastic variational Gaussian process regression, we develop a data-driven model to predict underwater transmission loss (TL, expressed in dB) for regions surrounding Vancouver Island and the Port of Vancouver. The intrinsic ability of Gaussian process regression to predict variance facilitates robust uncertainty quantification in TL predictions. Furthermore, the model is implemented in PyTorch to ensure differentiability, enabling seamless integration with ship route optimization frameworks to minimize the impact on marine mammals. We also developed an algorithm utilizing radial basis function interpolation to reconstruct TL data from scattered points.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".