arXiv (All)
open access
Equivariant Neural Prediction of the Stokes Resistance Tensors for Arbitrary Microparticle Shapes
The Stokes-flow hydrodynamics of an irregular microparticle is encoded by its grand resistance matrix, a 6x6 tensor whose translational and rotational blocks (A and C) govern settling, diffusion, and orientational transport. Empirical drag correlations compress these tensors to a single scalar, disc
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
- Brownian motion with geometry-dependent hydrodynamic memory#1011405
- Flow-Induced Intermittent Transport Shapes Colloid Filtration in Complex Media#1120549
- High-dimensional density estimation with tensorizing flow#1141372
- Stochastic modeling of particle structures in spray fluidized bed agglomeration using methods from machine learning / Lukas Fuchs, Sabrina Weber, Jialin Men, Niklas Eiermann, Orkun Furat, Andreas Bück, Volker Schmidt#1078566
- Lagrangian-Eulerian learning of flow field and trajectories with TrajectoryFlowNet#1122409
Record · ID 1048853
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