MiNO: Cotangent-bundle propagator learning for PDEs
Scientific machine learning for partial differential equations commonly targets solution fields, as in physics-informed neural networks, or solution maps, as in neural operators. We study a third target: the propagator itself, a phase and amplitude in phase space. The motivation is a gap in regularity. A transported discontinuity is nonsmooth in space and time, yet the rule that moves it can be a polynomial phase carrying unit amplitude, so the object that generates an evolution can be far smoother than the field it generates. The microlocal neural operator (MiNO) learns that object, using the eikonal equation for the phase and the transport equation for the amplitude, and recovers the so...
| Authors | Gnankan Landry Regis N'guessan, Bum Jun Kim |
|---|---|
| Primary category | cs.LG |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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