Chapter 20: Reproducible CCD and Future Directions

Building a common discipline rather than isolated case studies¶

CCD will mature through common benchmarks, explicit reporting, reusable interfaces, differentiable tools, physically grounded learning, experimental evidence, and system-level research questions.

Learning objectives¶

After completing this chapter, you should be able to:

1. explain and apply benchmarks and reports;

2. explain and apply model–solver interfaces;

3. explain and apply differentiable physics and learning;

4. explain and apply system synthesis;

5. formulate and verify the chapter methods on a reproducible repository with versioned models, data, formulations, solvers, tests, results, and hardware evidence.

Mathematical lens¶

The recurring quantities are interfaces, design and trajectory data, derivatives, metadata, seeds, tolerances, and provenance:

Running example¶

The recurring example is a reproducible repository with versioned models, data, formulations, solvers, tests, results, and hardware evidence. Retaining one system prevents apparent improvements from being caused by changed physics, information, loads, or metrics.

Recommended workflow¶

1. define benchmark.

2. publish interfaces.

3. capture environment.

4. reproduce evidence.

5. extend frontier.

Chapter map¶

1. Benchmark Problems

2. Standard Reporting Practices

3. Open-Source Software Organization

4. Reusable Model and Optimization Interfaces

5. Automatic Differentiation and Differentiable Simulation

6. Physics-Informed Learning

7. Reinforcement Learning Within CCD

8. System-Level Synthesis and Information Architecture

9. Distributed and Networked CCD

10. Human-in-the-Loop CCD

11. Digital Engineering and Model-Based Systems Engineering

12. Research Roadmap