Machine Learning for parameter estimation from Cellular Nonlinear Networks simulations

We present a lightweight 3D Convolutional Neural Network (3D-CNN) to estimate physical parameters from fluid dynamics simulations modeled with Cellular Nonlinear Networks (CeNNs) exploiting the inherent spatio-temporal structure of the data. By training on small spatio-temporal patches structured as 4D tensors, the Convolutional Neural Network captures both spatial and temporal patterns using artificial intelligence models based on 3D convolution operations. Despite its simplicity, the network accurately infers the viscosity and density parameters for unseen data. The method generalizes well and, thanks to its relatively small size, could be suitable for real-time or experimental use. Limitations, such as handling chaotic behavior or long-range temporal dependencies, are discussed as areas for future improvement.