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Double Pendulum: Chaos & Sensitivity to Initial Conditions (2D)

2D double-pendulum lab: RK4-integrated Lagrangian equations of motion, a live phase-space plot, and a twin pendulum released a fraction of a degree apart so you can watch chaotic divergence grow in real time.

AI & Machine Learning2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version
2d-identifying-machine-learning-problems-piers-framework-lab ↗ Open standalone

This 2D companion drives the same Lagrangian double-pendulum equations as the 3D version through a plain canvas view built for reading the physics rather than orbiting a scene: a side panel exposes both arm lengths, both masses, both release angles and a "chaos seed" offset, a phase-space plot traces θ2 against its angular velocity so you can see the system never repeats a loop, and a translucent twin pendulum — released a fraction of a degree away from the main one — visibly peels apart from it within seconds, making the sensitive dependence on initial conditions that defines chaotic systems directly visible instead of implied.

⚙ Under the hood

2D companion to the PIERS Framework Lab: score a candidate ML problem on a flat radar chart, switch between equal, business-priority and data-readiness weighting, and read a live composite score, threshold-pass count and leverage panel showing which factor is most worth fixing next.

machine learningproblem definitiondecision frameworkradar chartweighted scoring2d

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

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