arXiv (All)
open access
Geometry-native machine learning reconstruction of DSMC moment fields with support monitoring
Direct simulation Monte Carlo (DSMC) resolves rarefied-gas dynamics without a constitutive closure, but finite-sample estimates of macroscopic moments converge at markedly different rates. We develop a non-intrusive, geometry-native machine learning reconstruction of the retained two-dimensional mom
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling#1117382
- Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models#141482
- Probing Baryons with the Kinematic Sunyae Zel'dovich Effect and Machine Learning Derived Peculiar Velocities using DESI DR2 and ACT DR6#1131197
- JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics#684616
- JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics#682838
Record · ID 822075
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive