Developing Artificial Mechanics Intuitions from Extremely Small Data

TL;DR

This paper introduces a sample-switchable training method enabling artificial models to develop strong mechanics intuitions from as few as three samples, mimicking human learning efficiency.

Contribution

The paper presents a novel training approach that allows artificial systems to learn complex mechanics intuitions from minimal data, demonstrating significant predictive accuracy.

Findings

Models master brachistochrone, catenary, and elastic deformation problems with ≤3 samples

Intuitive prediction ability increases nonlinearly with more samples

Superb mechanics intuitions can be achieved from finite samples, akin to human learning

Abstract

Humans can possess good mechanics intuitions by learning from a few examples, which leads to the question of how to develop artificial mechanics intuitions that can be learned from small data, as we are eagerly entering the era of artificial intelligence. We propose in this Letter the sample-switchable training method, which successfully develops highly-accurate artificial mechanics intuitions that can master brachistochrone problem, catenary problem, and large nonlinear deformation problem of elastic plate by learning from no more than three samples. The model's intuitive prediction ability increases nonlinearly with respect to the number of training samples, suggesting that superb mechanics intuitions can be in-principle achieved based on a finite number of samples, reflecting how human brains form good mechanics intuitions just by learning a few cases. Our current work presents an alternative perspective for educating artificial intelligence capable of intuitively understand and predict how materials deform and move, a scenario that has been frequently seen in Science-Fiction movies.