Wavelet-Based Methods to Partition Multibody Systems With Contact in Dynamic Simulation
Bibliographic record
Abstract
Abstract The performance of physics simulation of multibody systems with contact can be enhanced by viewing the system as being composed of subsystems of bodies, and solving the dynamics of these subsystems in parallel. This approach to partition a system into subsystems, known as substructuring, is often based on topological information, such as the connectivity of a body in the system. However, substructuring based on topology may generate a potentially large number of equivalent decompositions, especially in highly symmetric systems, thus requiring a way to choose one partition over another. We propose that augmenting a topology-based partitioning scheme with dynamical information about the interactions between bodies may provide speedups by including temporal information about the constraint relationships between bodies. The simulation of multibody systems with contact typically exhibits nonstationary and multiscale interactions, which suggests a subsystem can be defined as a collection of bodies which have complex interactions with each other. We define complexity by introducing a novel metric based on the spread of time scales from a wavelet analysis of constraints between bodies. We show that for systems where purely topological information about the interaction between bodies is redundant, including dynamical information, not only removes redundancy but also can achieve significant computational speedups. Our results highlight the potential of using dynamical information to look at large-scale structures in multibody simulations.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".