Can You Predict Motion Without Understanding Physics?
Core Francisco ParkNovember 17, 20255 min read
physicschaosprediction
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Imagine you're shown a ball moving across your screen. You can see its trail, watch its current position, but you have no idea what's causing it to move. Can you predict where it will go next?
This is a fundamental question in machine learning and physics: Can you predict a system's behavior without understanding its underlying mechanics?
Behind the scenes, the full double pendulum system is being simulated:
The equations of motion are identical to a standard double pendulum - a chaotic system with sensitive dependence on initial conditions. But without seeing the structure, can you:
This visualization demonstrates a key challenge in many real-world problems:
This is similar to how machine learning models work:
This simulation raises a philosophical question about learning and understanding:
Is prediction without comprehension enough?
Modern AI systems can predict incredibly complex patterns without "understanding" the underlying physics. They're like an observer watching only the ball - they can learn correlations and patterns, but they don't grasp the elegant mathematical structure of the double pendulum.
In many practical applications, this might be sufficient. But for scientific understanding, for building robust systems that generalize, for developing intuition about the world - we need more than just pattern matching. We need the hidden structure.
The double pendulum is still there, swinging away, whether you can see it or not.