Towards a GPU-Based Simulation Framework for Deformable Surface Meshes
Bibliographic record
Abstract
Realism and real-time visual and haptic interactions with anatomical structures are key challenges in simulation software for surgeries. Overcoming these challenges is made difficult by the need to run the software on consumer-grade computing platforms. This paper presents preliminary work towards a framework for fast, realistic and stable simulation of deformable anatomical structures. The approach is to use implicit Euler's integration method to compute the deformations of the anatomy. This method is reformulated to exploit the inherent parallelism in the resulting simulation equations using Graphics Processing Unit (GPU) -a chip in many mid-range graphics cards. A dynamic surface mesh models the anatomy and a sparse, large linear system solved at each time step of the simulation models the deformations. The inverse of the large sparse matrix is computed as an approximation. The resulting large matrix manipulations are implemented on an NVIDIA GEForce 6600 GT GPU by using textures for storing mesh values and fragment-level programming for computing the new values due to deformations. The resulting simulation is compared with an explicit euler's integration-based simulation run on the CPU.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".