Talk Overview

This talk presents PathSim through its development journey: why it was created, what technical challenges it solves, and how it fits into the scientific Python ecosystem. Rather than just another simulation tool, PathSim addresses a specific gap between low-level ODE solvers and expensive proprietary environments.

Target Audience

• Engineers and researchers simulating physical systems
• Python developers curious about simulation frameworks
• Anyone who's hit limitations with scipy.integrate for complex systems
• Those interested in the journey of building scientific software

No simulation background required - concepts explained from first principles.

Talk Structure (30 minutes)

The Origin Story (4 min)

• Why build PathSim? Real problems that motivated development
• The gap between scipy.integrate and full simulation environments
• Design decisions: decentralized architecture, pure Python, minimal dependencies

What Makes Block Diagram Simulation Different? (8 min)

• Live comparison: same system with scipy.integrate vs PathSim
• Signal routing and interconnections
• The block diagram mental model for complex systems
• When you need structure beyond "write a function, call solve_ivp"

Core Features Through Examples (12 min)

Example 1: Event Handling

• Bouncing ball with zero-crossing detection
• Why this is hard with basic ODE solvers
• PathSim's event system in action

Example 2: Stiff Systems

• Van der Pol oscillator demonstration
• Implicit solver integration (ESDIRK, BDF methods)
• Adaptive timestepping visualization

Example 3: Hierarchical Modeling

• Building subsystems and nesting them
• Modularity for large-scale simulations

Integration & Ecosystem (4 min)

• Co-simulation capabilities and FMI support
• Hardware-in-the-loop applications
• Custom block development and extensibility
• Integration with other Python tools (control libraries, ML frameworks)

Getting Started (2 min)

• Installation and documentation
• Example gallery walkthrough
• Where PathSim fits in your workflow
• Contributing and community

Key Takeaways

• Understand when block diagram simulation adds value over basic ODE solving
• See practical examples of stiff systems and event handling
• Learn about PathSim's architecture and extensibility
• Know how to integrate simulation into broader Python workflows

Why This Talk Matters

Many Python users face the "scipy.integrate isn't enough, but I don't want MATLAB" problem. This talk shows there's a middle ground: structured simulation that's open source, Pythonic, and extensible. The development story provides insights into building scientific software, while practical demos show immediate applicability.

The talk emphasizes technical substance over credentials - showing what problems PathSim solves and how, rather than where it's being used.