Bibliographic record
Abstract
Simultaneous coupling of diverse physical systems poses significant computational challenges in terms of speed, quality, and stability. Rather than treating all components with a single discretization methodology (e.g., smoothed particles, material point method, Eulerian grid, etc.) that is ill-suited to some components, our solver, ElastoMonolith , addresses three-way interactions among standard particle-in-cell-based viscous and inviscid fluids, Lagrangian mesh-based deformable bodies, and rigid bodies. While prior methods often treat some terms explicitly or in a decoupled fashion for efficiency, often at the cost of robustness or stability, we demonstrate the effectiveness of a strong coupling approach that expresses all of the relevant physics within one consistent and unified optimization problem, including fluid pressure and viscosity, elasticity of the deformables, frictional solid-solid contact, and solid-fluid interface conditions. We further develop a numerical solver to tackle this difficult optimization problem, incorporating projected Newton, an active set method, and a transformation of the inner linear system matrix to ensure symmetric positive definiteness. Our experimental evaluations show that our framework can achieve high quality coupling results that avoid artifacts such as volume loss, instability, sticky contacts, and spurious interpenetrations.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".