Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks

TL;DR

Perception-Informed Neural Networks (PrINNs) extend physics-informed models by integrating perception-based information and expert knowledge, enabling neural networks to model complex, uncertain systems and discover new differential equations.

Contribution

PrINNs introduce a versatile framework that incorporates diverse perception data and expert knowledge into neural networks, advancing beyond traditional physics-informed approaches.

Findings

PrINNs effectively model dynamical systems with perception data.

They enable online training of fuzzy neural networks without pre-training.

PrINNs facilitate discovery of new differential equations.

Abstract

This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a platform for the integration of diverse forms of perception precisiation, including singular, probability distribution, possibility distribution, interval, and fuzzy graph. In fact, PrINNs allow neural networks to model dynamical systems by integrating expert knowledge and perception-based information through loss functions, enabling the creation of modern data-driven models. Some of the key contributions include Mixture of Experts Informed Neural Networks (MOEINNs), which combine heterogeneous expert knowledge into the network, and Transformed-Knowledge Informed Neural Networks (TKINNs), which facilitate the incorporation of meta-information for enhanced model performance. Additionally, Fuzzy-Informed Neural Networks (FINNs) as a modern class of fuzzy deep neural networks leverage fuzzy logic constraints within a deep learning architecture, allowing online training without pre-training and eliminating the need for defuzzification. PrINNs represent a significant step forward in bridging the gap between traditional physics-based modeling and modern data-driven approaches, enabling neural networks to learn from both structured physics laws and flexible perception-based rules. This approach empowers neural networks to operate in uncertain environments, model complex systems, and discover new forms of differential equations, making PrINNs a powerful tool for advancing computational science and engineering.