Inferring physical laws by artificial intelligence based causal models

TL;DR

This paper introduces a causal learning model that enables AI to infer physical laws by identifying cause-and-effect relationships, moving beyond mere data correlation analysis.

Contribution

It presents a novel causal inference approach for physical law discovery, integrating interventions to determine causality in physical phenomena.

Findings

Successfully identifies causal relationships in physical data

Distinguishes between correlation and causation in physical systems

Enhances confidence in physical models through causal analysis

Abstract

The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of associations and boil down to complex data fitting. Judea Pearl has pointed out that Artificial General Intelligence must involve interventions involving the acts of doing and imagining. Any machine assisted scientific discovery thus must include casual analysis and interventions. In this context, we propose a causal learning model of physical principles, which not only recognizes correlations but also brings out casual relationships. We use the principles of causal inference and interventions to study the cause-and-effect relationships in the context of some well-known physical phenomena. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables, thereby strengthening (or weakening) our confidence in the proposed model of the underlying physical process.