Exponential elastic model and its application in real-time simulation
Bibliographic record
Abstract
The development of accurate material models and computational methods are two fundamental components in building a real-time realistic surgery simulator. In this paper, we use a least-squares method to calibrate an exponential model of pig liver based on the assumption of incompressible material under a uniaxial testing mode. With the obtained parameters, the stress-strain curves generated from the least-squares approach are compared to those from the corresponding model built in ABAQUS and to experimental data, resulting in mean deviations of 1.9% and 4.8%, respectively. Furthermore we demonstrate equivalence between the parameters of the exponential material model and those of linear or other nonlinear models under small strains. Finally, we incorporate this calibrated exponential model into a nonlinear finite element framework to simulate the behavior of liver during an interventional procedure, and achieve real-time performance through use of an interpolation approach.
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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".