Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operatorsQile Yan,

Anran Jiao, Qile Yan, John Harlim, Lu Lu

Abstract:

In this paper, we evaluate the effectiveness of deep operator networks (DeepONets) in solving
both forward and inverse problems of partial differential equations (PDEs) on unknown manifolds.
By unknown manifolds, we identify the manifold by a set of randomly sampled data point clouds
that are assumed to lie on or close to the manifold. When the loss function incorporates the
physics, resulting in the so-called physics-informed DeepONets (PI-DeepONets), we approximate
the differentiation terms in the PDE by an appropriate operator approximation scheme. For the
second-order elliptic PDE with a nontrivial diffusion coefficient, we approximate the differentiation term with one of these methods: the Diffusion Maps (DM), the Radial Basis Functions
(RBF), and the Generalized Moving Least Squares (GMLS) methods. For the GMLS approximation,
which is more flexible for problems with boundary conditions, we derive the theoretical error
bound induced by the approximate differentiation. Numerically, we found that DeepONet is
accurate for various types of diffusion coefficients, including linear, exponential, piecewise linear,
and quadratic functions, for linear and semi-linear PDEs with/without boundaries. When the
number of observations is small, PI-DeepONet trained with sufficiently large samples of PDE
constraints produces more accurate approximations than DeepONet. For the inverse problem,
we incorporate PI-DeepONet in a Bayesian Markov Chain Monte Carlo (MCMC) framework to
estimate the diffusion coefficient from noisy solutions of the PDEs measured at a finite number
of point cloud data. Numerically, we found that PI-DeepONet provides accurate approximations
comparable to those obtained by a more expensive method that directly solves the PDE on the
proposed diffusion coefficient in each MCMC iteration.