# [Principle Title]

**Layer:** L1 Principle
**Domain:** [e.g., Optical Imaging, Quantum Sensing, Acoustic Imaging]
**Task-type tags:** [select 1+: design / sci-sim / reconstruct / mismatch]
**Stake to register:** max(10 PWM, ceil($50 / TWAP_30d))

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## Understanding This Principle — Plain Language

### The Big Idea
[Explain the core concept in 1-2 sentences. Assume the reader is a smart undergraduate.]

### Why It Matters
[Why is this principle important? What problem does it solve?]

### Real-World Applications
- [Application 1: medical, industrial, etc.]
- [Application 2]
- [Application 3]

### Who Uses It
[Target user populations]

### What an Algorithm Has to Do

**Given:**
- [Input 1]
- [Input 2]

**Find:**
- [Output 1]
- [Output 2]

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## Visual Example

[Insert a side-by-side diagram or describe what users would see.]

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## Technical Details — Math (Collapsible)

### Forward Model
[Mathematical equations. e.g., y = H x + n with definitions of each term.]

### Reconstruction Objective
[How algorithms approach this. e.g., x* = argmin_x ||y - H x||^2 + λ R(x).]

### Well-posedness Analysis
[Theoretical guarantees: existence, uniqueness, stability.]

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## Comparison to Related Principles

[Comparison table to related L1 principles, if any.]

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## References

[Academic citations, key papers, canonical textbooks.]

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## Standards Met

- [ ] Plain-language explanation present
- [ ] Real-world applications listed
- [ ] Visual example provided
- [ ] Math included for researchers
- [ ] References cited

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## AI Authoring Notes

Notes for ChatGPT / Claude / other AI tools:

> "Make this principle clear and educational. Include real-world examples relevant to my domain: **{user fills in their specific domain}**. Focus on understandability for a smart undergraduate. Then add a researcher-grade technical section with explicit math. Cite at least 3 canonical references. Follow the section structure above exactly — do not invent new sections or reorder them. Return the result as a complete MD file."