Principles

 I have been exploring a different way to think about physics in simulations. Instead of using object-oriented structures where each physical entity carries its own full behavior, I want to build a library of pure “first-principles forces.” Each force is its own small module, ideally a compact neural network, and each module acts like a tiny brain center that reads object properties and produces its own contribution to the world.

Objects in this system are nothing more than numeric data points. A water droplet, a dust particle, a photon, or a molecule is just a bundle of values such as position, velocity, mass, charge, temperature, or phase. No object contains its own methods. All behavior comes from the outside, from many small force networks evaluating the state at each step.

The simulation loop becomes simple. It holds a list of objects and a list of forces. Each force module looks at the current state and writes an influence, such as a force vector, a heat change, or a probability of phase transition. After all influences are collected, the system updates every object. The world is the sum of these small brains acting together.

This idea becomes especially attractive when dealing with very large numbers of tiny objects, such as water molecules nucleating into a droplet or photons passing through a medium. Classical physics code becomes messy when hundreds of thousands of items interact. A neural approach, where each phenomenon is a learned transformation, scales more naturally and can approximate complex interactions without hand-written formulas.

The long-term vision is a library of first-principles modules: inertia, drag, friction, buoyancy, pairwise gravity, electrostatic influence, thermal conduction, phase changes, and so on. Each module would be replaceable: analytic formulas when known, neural networks when unknown or too expensive to compute directly. This creates a flexible framework where simple rules, traditional physics, and learned behavior coexist.

For now this remains a conceptual sketch, but it suggests a path toward simulations that are more modular, scalable, and expressive. I can return to this idea later and explore how to structure the object schemas, how to train the small neural force networks, and how to combine them into a stable and useful physics engine.