Fast mathematical modeling of partial-breach dam-break flow using a time-series field-reconstruction deep learning approach
Bibliographic record
Abstract
Mathematical modeling of dam-breach flow can provide a better understanding of dam failure events, which in turn helps people to reduce potential losses. In the present study, the smooth particle hydrodynamics (SPH) modeling approach was employed to simulate the three-dimensional (3D) partial-breach dam-break flow using two different viscosity models: the artificial viscosity and sub-particle-scale models. The validated and best-performing SPH model was further employed to conduct numerical experiments for various scenarios, which generated a comprehensive dataset. The current work also presents a novel time-series field-reconstruction deep learning (DL) approach: Time Series Convolutional Neural Input Network (TSCNIN) for modeling the transient process of partial-breach dam-break flow and for providing the complete flow field. This approach was constructed based on the long short-term memory and convolutional neural network algorithms with additional input layers. A DL-based model was trained and validated using the numerical data, and tested using two additional unseen scenarios. The results demonstrated that the DL-based model can accurately and efficiently predict the transient water inundation process, and model the influence of dam-break gaps. This study provided a new avenue of simulating partial-breach dam-break flow using the time-series DL approaches and demonstrated the capability of the TSCNIN algorithm in reconstructing the complete fields of transient variables.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".