Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems

Xuhui MENG

doi:10.1007/s10483-023-2997-7

2023 , Vol. 44 >Issue 7: 1111 - 1124

DOI: https://doi.org/10.1007/s10483-023-2997-7

Articles

Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems

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Institute of Interdisciplinary Research for Mathematics and Applied Science, School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China

Received date: 2023-02-22

Revised date: 2023-05-28

Online published: 2023-07-05

Supported by

the National Natural Science Foundation of China (No. 12201229)

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Abstract

Physics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such models. The former encodes the physical laws via the automatic differentiation, while the latter learns the hidden physics from data. Generally, the noisy and limited observational data as well as the over-parameterization in neural networks (NNs) result in uncertainty in predictions from deep learning models. In paper "MENG, X., YANG, L., MAO, Z., FERRANDIS, J. D., and KARNIADAKIS, G. E. Learning functional priors and posteriors from data and physics. Journal of Computational Physics, 457, 111073 (2022)" has two stages: (i) prior learning, and (ii) posterior estimation. At the first stage, the GANs are utilized to learn a functional prior either from a prescribed function distribution, e.g., the Gaussian process, or from historical data and available physics. At the second stage, the Hamiltonian Monte Carlo (HMC) method is utilized to estimate the posterior in the latent space of GANs. However, the vanilla HMC does not support the mini-batch training, which limits its applications in problems with big data. In the present work, we propose to use the normalizing flow (NF) models in the context of variational inference (VI), which naturally enables the mini-batch training, as the alternative to HMC for posterior estimation in the latent space of GANs. A series of numerical experiments, including a nonlinear differential equation problem and a 100-dimensional (100D) Darcy problem, are conducted to demonstrate that the NFs with full-/mini-batch training are able to achieve similar accuracy as the "gold rule" HMC. Moreover, the mini-batch training of NF makes it a promising tool for quantifying uncertainty in solving the high-dimensional partial differential equation (PDE) problems with big data.

Key words： uncertainty quantification (UQ); physics-informed neural network (PINN); deep operator network (DeepONet); generative adversarial network (GAN); normalizing flow (NF); differential equation

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Xuhui MENG . Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems[J]. Applied Mathematics and Mechanics, 2023 , 44(7) : 1111 -1124 . DOI: 10.1007/s10483-023-2997-7

References

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