Non-equilibrium phenomena implemented at a mesoscopic time scale
Bibliographic record
Abstract
The purpose of this project is to develop an algorithm that speeds up large scale simulations of many-body systems. A numerical method is implemented that simulates non-equilibrium phenomena on a mesoscopic time scale. A system is perturbed by an external force, and time averages of variables renormalized in space are calculated numerically, using results of linear response theory, as the system relaxes to equilibrium. The coarse-grained variables evolve slowly in time, allowing one to advance them on a mesoscopic time scale. The algorithm was tested on two physical systems: a lattice confined ferromagnetic Ising model and an off-lattice Argon-like molecular system. The method simulated accurately the non-equilibrium phenomena studied. It was found that the algorithm is most efficient when it is applied to a process occurring on at least two time scales. This allows one to integrate out the fast, microscopic time scale in order to study long-time, macroscopic behaviour. Through the study of diffusion in a molecular system, it was concluded that the proposed method is computationally faster than solving the microscopic equations of motion and more accurate than solving the macroscopic equations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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; both teacher heads agree on what is shown here.
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".