Effective Geometry Monte Carlo: A Fast and Reliable Simulation Framework for Molecular Communication
Bibliographic record
Abstract
Angular information of messenger molecules absorbed by a receiver plays a significant role in the molecular communication literature. In this paper, we address systematic biases and random errors in the angular information stemming from finite step sizes encountered in traditional simulation frameworks. We show that the effective geometry Monte Carlo (EG-MC) simulation algorithm, which modifies the geometry of the receiver, is a fast and reliable simulation method to overcome these systematic biases. We motivate our approach for a 3-D unbounded diffusion channel consisting of an absorbing receiver and a point transmitter. We show that, with minimal computational cost, the angular distribution of the absorbed particles by the receiver can be precisely obtained using EG-MC algorithm. Afterwards, we demonstrate the accuracy of our simulations and compare them to traditional methods. Then, we comment on the range of applicability of our results. Finally, we consider two simple cases with constant flow and show that the EG-MC algorithm gives consistent results even when the drift is dominant over diffusion. We conclude with further remarks on the computational efficiency and reliability of the EG-MC method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".