An improved periodic activation for PINNs reconstructing convective flows
Architectures with periodic activation functions have already been shown to be beneficial in comparison to monotonic counterparts for a wide range of applications of physics-informed neural networks. Here, we investigate a network architecture which uses the complex exponential function, generating pairs of sine and cosine outputs as activation functions. Testing it against comparable, sine-activated multi-layer perceptrons for the task of temperature reconstruction from sparse velocity data for cubic Rayleigh-Bénard convection reveals significant improvements in the reconstruction quality without a substantial increase in computational cost per training step. Vice versa, the improved arc...
| Authors | Michael Mommert, Marie-Christine Volk, Christian Bauer |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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