Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows

arXiv:2607.22280 · physics.flu-dyn, cs.AI · Submitted 2026-07-24 · Read on arXiv

physics.flu-dyn, cs.AI

Submitted: 2026-07-24

Updated: 2026-10-18

Terminology

• Benchmarking — 2× in this paper · explained in 4 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Surrogate Model — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Related papers

• Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
• Physics Foundation Models for Efficient Control of Fluid Flows
• Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
• Fast localized urban microclimate emulator: wind and temperatures in unseen urban morphologies
• Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
• Diffraction of walking drops by a standing Faraday wave