Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators

TL;DR

This paper introduces a Bayesian method for quantifying uncertainty caused by noisy inputs and outputs in physics-informed neural networks (PINNs) and neural operators (NOs), enhancing their reliability in scientific applications.

Contribution

The paper presents a novel Bayesian approach that seamlessly integrates with PINNs and NOs to address uncertainty from noisy inputs, a challenge less explored in prior work.

Findings

Method effectively quantifies input-output uncertainty in PINNs and NOs.

Enables PINNs to handle noisy spatial-temporal coordinates.

Allows neural operators to manage noisy input functions.

Abstract

Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines. Representative SciML models are physics-informed neural networks (PINNs) and neural operators (NOs). While UQ in SciML has been increasingly investigated in recent years, very few works have focused on addressing the uncertainty caused by the noisy inputs, such as spatial-temporal coordinates in PINNs and input functions in NOs. The presence of noise in the inputs of the models can pose significantly more challenges compared to noise in the outputs of the models, primarily due to the inherent nonlinearity of most SciML algorithms. As a result, UQ for noisy inputs becomes a crucial factor for reliable and trustworthy deployment of these models in applications involving physical knowledge. To this end, we introduce a Bayesian approach to quantify uncertainty arising from noisy inputs-outputs in PINNs and NOs. We show that this approach can be seamlessly integrated into PINNs and NOs, when they are employed to encode the physical information. PINNs incorporate physics by including physics-informed terms via automatic differentiation, either in the loss function or the likelihood, and often take as input the spatial-temporal coordinate. Therefore, the present method equips PINNs with the capability to address problems where the observed coordinate is subject to noise. On the other hand, pretrained NOs are also commonly employed as equation-free surrogates in solving differential equations and Bayesian inverse problems, in which they take functions as inputs. The proposed approach enables them to handle noisy measurements for both input and output functions with UQ.