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Physics-Informed Neural Digital Twin — 2D

A 2D canvas digital-twin heat-field reconstructed by a tiny RBF neural network from only a handful of sparse sensors, regularized by the governing PDE. Watch a physics-informed loss beat a pure data-fit as you dial the physics weight.

AI & Machine Learning2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

A real thermal plate is solved in full on the left — every grid cell, every frame, straight from the 2D steady heat equation — and drawn as a color heatmap with isotherm contour lines. On the right, a tiny RBF network never sees that grid at all: it only gets readings from a sparse handful of sensors (the yellow dots), plus a physics-informed loss term that penalizes it for disobeying the same governing PDE anywhere in the domain. Drag the physics-loss weight λ down to zero, let training run for a while, and watch the reconstruction ring and overshoot between sensors as it interpolates them almost exactly; raise λ back up and the same trained network snaps back toward the smooth true field — a working, from-scratch model of how physics-informed digital twins reconstruct a full physical state from only a few real-world sensors.

⚙ Under the hood

A tiny RBF neural network reconstructs a full 2D heat field from only a handful of sparse sensors, drawn as side-by-side color heatmaps with isotherm contour lines — drag the physics-loss weight to zero and let training run long enough to interpolate the sensors, then watch the reconstruction ring between them, and raise the weight back up to see it snap back to the true field.

digital twinphysics-informed neural networkPINNsensor fusionsurrogate modelheat equation2D heatmapisotherm contours

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install