> ## Documentation Index
> Fetch the complete documentation index at: https://docs.veydra.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Calibration Mode

> Fine-tune your model parameters to match real-world data using manual, automated, and data-driven methods

## Overview

Calibration is the process of adjusting your model's parameters so that its outputs match observed real-world data or expected behavior. In Veydra, calibration happens in **Calibrate mode** — a dedicated workspace with three approaches: manual tuning, automated optimization, and data-driven fitting.

<Tip>
  Parameter changes in Calibrate mode apply to both the **baseline** and **current scenario**. This is different from Experiment mode, where changes only affect the current scenario.
</Tip>

## Calibration Panel

When you switch to Calibrate mode, a dedicated Calibration Panel opens on the left side of the playground. It offers three tabs:

<CardGroup cols={3}>
  <Card title="Manual" icon="sliders">
    Adjust parameters by hand and observe effects in real time
  </Card>

  <Card title="Auto" icon="cpu">
    Automated optimization using algorithms (coming soon)
  </Card>

  <Card title="Data" icon="chart-line">
    Upload observed data and calibrate against it (coming soon)
  </Card>
</CardGroup>

***

## Manual Calibration

Manual calibration gives you direct control over every parameter in your model. This is the recommended starting point — it builds intuition about which parameters matter most and how they influence model behavior.

### How It Works

1. **Open Calibrate mode** — Click the Calibrate button in the top navigation bar
2. **Expand a submodel section** — Parameters are grouped by submodel; click any section to expand it
3. **Adjust a parameter** — Use the slider or type a value directly
4. **Run the model** — See how the output changes in the chart area
5. **Compare against baseline** — Use Compare Mode to see before/after

### What You See

* **Parameter sliders** organized by submodel (all sections start collapsed so you can navigate to the group you need)
* **Charts** showing real-time simulation output as you adjust values
* **Stock-Flow Diagram** available for reference (minimized by default)

### Tips for Effective Manual Calibration

<AccordionGroup>
  <Accordion title="Start with the most influential parameters">
    Focus on parameters that have the largest impact on model behavior. Stocks' initial values and key flow rates typically matter most.
  </Accordion>

  <Accordion title="Use Compare Mode">
    Keep Compare Mode enabled to see how your current parameter values differ from the baseline. This makes it easy to spot improvements and regressions.
  </Accordion>

  <Accordion title="Work one submodel at a time">
    Expand one parameter group, make adjustments, observe the effect, then move on. This avoids confusion about which change caused which effect.
  </Accordion>

  <Accordion title="Reset if needed">
    Use the reset button to return parameters to their defaults. This gives you a clean starting point without losing your scenario history.
  </Accordion>
</AccordionGroup>

***

## Automated Calibration (Coming Soon)

<Info>
  Automated calibration is not yet enabled. Contact sales to learn about activating this capability for your organization.
</Info>

Automated calibration uses optimization algorithms to find the best-fit parameter values. Instead of adjusting sliders by hand, you define which stocks to optimize and let the system search for optimal values.

### Planned Features

* **Optimization methods** — Genetic Algorithm, Gradient Descent, and Bayesian Optimization
* **Stock selection** — Choose which model stocks to include in the optimization objective
* **Iteration control** — Set maximum iterations and convergence criteria
* **Progress tracking** — Monitor optimization progress in real time

### Typical Workflow

<Steps>
  <Step title="Select method">
    Choose an optimization algorithm based on your model's characteristics
  </Step>

  <Step title="Configure stocks">
    Select which stocks the optimizer should focus on
  </Step>

  <Step title="Run optimization">
    The system iterates through parameter combinations to minimize error
  </Step>

  <Step title="Review results">
    Compare the optimized output against your target data
  </Step>
</Steps>

<Tip>
  Start with manual calibration to understand your model's sensitivity, then use automated tools for fine-tuning. Manual exploration will help you set reasonable bounds for the optimizer.
</Tip>

***

## Data-Driven Calibration (Coming Soon)

<Info>
  Data-driven calibration is not yet enabled. Contact sales to learn about activating this capability for your organization.
</Info>

Data-driven calibration lets you upload historical or observed data and automatically fit your model's parameters to match it. This is the most rigorous approach — especially useful when you have time-series data from the real system you're modeling.

### Planned Features

* **CSV / Excel / JSON upload** — Bring your own time-series data
* **Automatic column mapping** — Match data columns to model stocks
* **Fit quality metrics** — RMSE, R², and visual overlay of model vs. data
* **Iterative refinement** — Adjust the fit and re-run as needed

***

## Calibration vs. Experimentation

Understanding the difference between Calibrate and Experiment modes is key to using Veydra effectively:

|                              | Calibrate Mode                                                | Experiment Mode                              |
| ---------------------------- | ------------------------------------------------------------- | -------------------------------------------- |
| **Purpose**                  | Match the model to reality                                    | Explore what-if scenarios                    |
| **Parameter changes affect** | Both baseline and current scenario                            | Current scenario only                        |
| **Baseline**                 | Being defined                                                 | Fixed (from calibration)                     |
| **Typical question**         | "What parameter values make the model match historical data?" | "What happens if we double the growth rate?" |

The recommended workflow is: **Design → Calibrate → Experiment → Decide**.

## Next Steps

<CardGroup cols={2}>
  <Card title="Playground Overview" icon="play" href="/playground/overview">
    Return to the full playground documentation
  </Card>

  <Card title="Model Controls" icon="sliders" href="/components/model-controls">
    Learn about parameter sliders, activation, and scenario management
  </Card>

  <Card title="Behavior Analysis" icon="magnifying-glass-chart" href="/tools/behavior-analysis">
    Analyze time-series patterns to validate your calibration
  </Card>

  <Card title="Experiment Mode" icon="chart-line" href="/playground/overview#experiment-mode">
    After calibrating, explore scenarios in Experiment mode
  </Card>
</CardGroup>