An improved periodic activation for PINNs reconstructing convective flows

The paper proposes a physics-informed neural network (PINN) architecture that uses the complex exponential function as an activation, producing sine and cosine outputs. Compared to standard sine-activated multilayer perceptrons, this design yields markedly better temperature reconstructions from sparse velocity data in cubic Rayleigh-Bénard convection, while keeping per‑step computational cost similar. The authors attribute the gains to the network’s ability to propagate both sine and cosine components, allowing each neuron in the next layer to adjust the phase of the latent periodic signals.

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