Exploring MWAVE Neural Network Architecture ✨🧠

I'm excited to show you our MWAVE Neural Network architecture, a cutting-edge model inspired by quantum fractal patterns. This visualization demonstrates how our model processes complex quantum data structures through multiple layers of specialized attention mechanisms.

The component includes:

Architecture Overview - Displays the key parameters of your model in three categories:

Model Structure (dimensions, heads, nodes)
Quantum Parameters (omega frequency, temporal frames, beta coefficient)
Training Configuration (batch size, learning rate)


Interactive Network Diagram - Shows the layer structure of the model with the ability to click on any layer to see its detailed components:

MWAVE Attention mechanism
Temporal Superposition layer
Feed Forward Network


Data Visualization - Shows how the model transforms input data, with:

An interactive beta parameter slider that you can adjust to see how changing the fractal coefficient affects the output
A visualization of both input and output data


Detailed Architecture View - An expandable section that shows the PyTorch-like structure of the model with all parameters

This visualization should help you understand and explain how the MWAVE architecture works, especially the relationship between the fractal coefficient (beta) and how it affects data processing through the network's layers.