48 lines
1.8 KiB
Markdown
48 lines
1.8 KiB
Markdown
# Architecture Overview
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## System Diagram (text)
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```
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+--------------------+
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| Program Generator |
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| (Type 1 setpoints) |
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+---------+----------+
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v
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+-----------------------+
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| Controller / Actuator |
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| (Type 2/3 modules) |
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+---------+-------------+
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v
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+----------------------------+
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| Diffusion (Residuals) |
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| - Continuous PVs |
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| - Discrete mask diffusion |
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+---------+------------------+
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v
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+-----------------------------+
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| Post-processing |
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| - Derived tags (Type 5) |
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| - KS-only resample baseline |
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+-----------------------------+
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```
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## Core Components
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- **Stage‑1 Temporal GRU**: learns trend for continuous features.
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- **Diffusion Backbone**: Transformer (default) or GRU; predicts residuals + discrete logits.
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- **Post-hoc Calibration**: optional quantile calibration to align 1D CDFs.
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- **KS-only Baseline**: Type1/2/3/5/6 empirical resampling for rapid KS reduction (diagnostic; may hurt joint realism).
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## Feature-Type Split
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1) **Type 1**: Setpoints/demands → program generator
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2) **Type 2**: Controller outputs → small emulator / conditional head
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3) **Type 3**: Actuators/valves → spike‑and‑slab / dwell-time model
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4) **Type 4**: Process PVs (multi‑modal/heavy tail) → diffusion with conditioning
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5) **Type 5**: Derived tags → deterministic reconstruction (or empirical KS baseline)
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6) **Type 6**: Auxiliary/vibration → narrow-band AR/SSM or empirical KS baseline
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## Data Flow
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- Input CSV → stats/vocab → normalized batches
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- Trend GRU → residual diffusion → inverse transforms → export
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