Data scientists often jump straight to machine learning when tackling a new problem. But there's a foundational step that can dramatically increase your chances of project success and create more reliable business value. Mathematical modelling from first principles provides a low-cost scaffolding that can make your machine learning work more robust.

In this Value Boost episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how building models from physics principles, like mass and energy conservation, creates a modular foundation that reduces computational costs and makes your work easier to understand.

In this episode, we explore:
1. What mathematical modelling from first principles actually means [01:20]
2. How to build modular models with different resolution levels [04:39]
3. When to add machine learning to first principles models [08:18]
4. The practical first step to incorporate this approach into your work [09:23]

Guest Bio

Dr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world’s first GAMSPy course.

Links

• Bluebird Optimization Website
• Connect with Genevieve on LinkedIn
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