First Principles Models

First Principles Models are computational or mathematical models built from fundamental physical, chemical, or biological laws, rather than relying on empirical data or historical patterns. They simulate real-world systems by solving equations derived from basic principles, such as conservation laws or quantum mechanics. This approach is used in fields like engineering, physics, and finance to predict system behavior under novel conditions or when data is scarce.

Also known as: First-Principles Models, First Principles Modeling, Ab Initio Models, Physics-Based Models, Fundamental Models

Why learn First Principles Models?

Developers should learn First Principles Models when working on simulations, predictive analytics, or systems where empirical data is unavailable, unreliable, or insufficient for training machine learning models. They are crucial in high-stakes domains like aerospace, climate science, or drug discovery, where accuracy and interpretability are paramount, and in research to validate data-driven approaches against theoretical foundations.

Learning Resources

• docs: First Principles Modeling: A Comprehensive Guide
• course: Introduction to First Principles in Physics and Engineering
• video: First Principles vs. Data-Driven Models in Science
• book: Numerical Methods for First Principles Simulations