Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows
Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics. Developing reliable machine learning surrogates for these flows remains challenging due to the simultaneous presence of compressibility, sharp discontinuities, and multiphase effects. In this work, we introduce the first large-scale benchmark specifically designed for shock-driven compressible multiphase flows, comprising 2.4 TB of high-fidelity 2D and 3D datasets featuring shock-induced bubble collapse and droplet breakup. We evaluate diverse sur...
| Authors | Harish Ramachandran, Björn Kimpel, Thomas Paula et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 24 July 2026 |
| Last updated at source | 27 July 2026 |
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