Physics-informed deep learning for topological phononic crystal design. Adjust parameters below to compute band structures, predict bandgaps, and classify topological phases in real time — all running client-side in your browser.
L = Ldata + λ · Lphysics
Helmholtz equation residual enforces wave physics during training. Bragg scattering constraints ensure physical consistency.
Input(4) → Embed(128) → 4×ResBlock → Heads
Skip connections improve gradient flow in the non-convex physics-informed loss landscape.
Attention → MLP(128,64,32) → σ
Feature attention learns which crystal parameters drive topological phase transitions.