Simulation of Wave Propagation in Biomimetic Porous Scaffold Using Artificial Neural Network
Bibliographic record
Abstract
Abstract The study of wave propagation in biomimetic porous scaffold requires the inclusion of some complex physics such as the interaction of the ultrasonic wave with pore fluid, solid phase, and porous material. Also, due to viscous interactions between the pore fluid and skeletal frame, the dynamic tortuosity as a fractional function of frequency in the clinically relevant ultrasound frequency range is considered. The bone scaffold here is simulated using a porous slab whose two dimensions are infinite. The Biot-JKD theory used for wave propagation in porous media is conditioned with many physical parameters. Solving such governing equations for complex multi-physics problems is computationally expensive. Therefore, developing efficient tools and numerical methods to address multi-physics problems is appealing. Artificial Neural Network (ANN) can efficiently solve convoluted-parametric problems. The purpose of this research is to propose a physics-aware ANN to simulate wave propagation in bone scaffold filled with a viscous fluid. A set of data including porosity, viscosity, tortuosity, viscous characteristics length, Poisson’s ratio, and elastic modulus which are sensitive to the transmission and reflection signals are applied to the ANN as inputs and the reflection and transmission signals are obtained as outputs. The reflected and transmitted waves for different porosities are considered and the results show an excellent agreement with the proposed analytical theory and experimental data found in the literature.
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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 | 0.000 |
| 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".