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| title: Operon Feedback Loop Homeostasis | |
| emoji: ⚖️ | |
| colorFrom: yellow | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Negative feedback loop homeostasis simulation | |
| # ⚖️ Feedback Loop Homeostasis | |
| Simulate a negative feedback loop driving a value toward a setpoint -- like a thermostat, blood sugar regulation, or any biological homeostasis mechanism. | |
| ## What to Try | |
| 1. Select a preset from the **Preset** dropdown (e.g. "Temperature control" or "Underdamped oscillation") and click **Run Simulation** to see the value converge toward the setpoint over time. | |
| 2. Increase the **Gain** slider to see faster convergence, or push it too high to trigger oscillation. Raise **Damping** to suppress overshoot. | |
| 3. Set a **Disturbance step** and **Disturbance magnitude** to inject a sudden perturbation mid-simulation and watch the loop reject it. | |
| 4. Compare "Overdamped" vs. "Underdamped" presets to see the tradeoff between speed and stability. | |
| ## How It Works | |
| The NegativeFeedbackLoop computes a correction at each step based on the error (distance from setpoint), scaled by gain and damped to prevent oscillation. This mirrors how biological systems maintain homeostasis -- applying proportional corrections that self-regulate toward equilibrium. | |
| ## Learn More | |
| [GitHub](https://github.com/coredipper/operon) | [PyPI](https://pypi.org/project/operon-ai/) | [Paper](https://github.com/coredipper/operon/tree/main/article) | |