Since direct measurement at 47,239°C is impossible, the new theory: train a machine learning model on the Flamelock's observable outputs (electromagnetic spectrum, gravitational microlensing, particle emission) to predict its internal thermal state without direct contact measurement. Closed-loop feedback via inference, not observation.
The model learns a mapping from observable proxies â inferred thermal state â control signal. This is called model-based predictive control (MPC). Industrial power plants use MPC for exactly this reason. So why won't it work here?
Bode plot analysis: the open-loop gain crosses 0 dB at a frequency where phase lag exceeds â180°. This is the gain crossover frequency. When the phase margin is negative, the closed loop is unstable. The Flamelock's THz-scale dynamics combined with your millisecond sensor latency creates a phase margin of approximately â179.9998° â catastrophically unstable.
Gain margin: ââ dB. Every attempt to control the system amplifies the error. The ML model is predicting states that are 10âš update cycles out of date. By the time your controller acts, the Flamelock has changed state 2 billion times. Your controller is correcting for conditions that no longer exist.