Modeling human intuitions about liquid flow with particle-based simulation

TL;DR

This paper introduces a particle-based computational model that mimics human intuition in predicting complex liquid behaviors, outperforming heuristic and neural network models in accuracy and explaining variations based on fluid properties.

Contribution

The study presents a novel particle simulation model that captures human predictions of liquid flow, extending previous work on rigid objects to fluid dynamics.

Findings

Model accurately predicts liquid flow among obstacles

Outperforms heuristic and neural network models

Explains prediction variations based on fluid properties

Abstract

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a "game engine in the head", drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people's predictions about how liquids flow among complex solid obstacles, and was significantly better than two alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people's predictions varied as a function of the liquids' properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.