An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation
This work provides a Bayesian interpretation for physics-informed methods, which is incremental but useful for researchers in scientific computing and uncertainty quantification.
The authors tackled the lack of a direct Bayesian connection in physics-informed machine learning by showing that Brownian bridge Gaussian processes serve as a physics-constrained prior for the Poisson equation, enabling theoretical analysis of convergence and inverse problems.
Physics-informed machine learning is one of the most commonly used methods for fusing physical knowledge in the form of partial differential equations with experimental data. The idea is to construct a loss function where the physical laws take the place of a regularizer and minimize it to reconstruct the underlying physical fields and any missing parameters. However, there is a noticeable lack of a direct connection between physics-informed loss functions and an overarching Bayesian framework. In this work, we demonstrate that Brownian bridge Gaussian processes can be viewed as a softly-enforced physics-constrained prior for the Poisson equation. We first show equivalence between the variational form of the physics-informed loss function for the Poisson equation and a kernel ridge regression objective. Then, through the connection between Gaussian process regression and kernel methods, we identify a Gaussian process for which the posterior mean function and physics-informed loss function minimizer agree. This connection allows us to probe different theoretical questions, such as convergence and behavior of inverse problems. We also connect the method to the important problem of identifying model-form error in applications.