A neural operator framework for data-driven discovery of stability and receptivity in physical systems
physics.flu-dyn, cs.AI
Submitted: 2026-04-21
Updated: 2026-10-19
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