Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations

• URL: http://arxiv.org/abs/2503.18012v1
• Date: Sun, 23 Mar 2025 10:19:26 GMT
• Title: Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
• Authors: Shaoqian Zhou, Wen You, Ling Guo, Xuhui Meng,
• Abstract summary: A physics-informed deep generative model (sPI-GeM) is capable of solving SDE problems with both high-dimensional and spatial space.<n>The sPI-GeM addresses the scalability in the spatial space in a similar way as in the widely used dimensionality reduction technique.
• Score: 0.9419294043578184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs with high-dimensional spatial space. In the present study, we propose a scalable physics-informed deep generative model (sPI-GeM), which is capable of solving SDE problems with both high-dimensional stochastic and spatial space. The sPI-GeM consists of two deep learning models, i.e., (1) physics-informed basis networks (PI-BasisNet), which are used to learn the basis functions as well as the coefficients given data on a certain stochastic process or random field, and (2) physics-informed deep generative model (PI-GeM), which learns the distribution over the coefficients obtained from the PI-BasisNet. The new samples for the learned stochastic process can then be obtained using the inner product between the output of the generator and the basis functions from the trained PI-BasisNet. The sPI-GeM addresses the scalability in the spatial space in a similar way as in the widely used dimensionality reduction technique, i.e., principal component analysis (PCA). A series of numerical experiments, including approximation of Gaussian and non-Gaussian stochastic processes, forward and inverse SDE problems, are performed to demonstrate the accuracy of the proposed model. Furthermore, we also show the scalability of the sPI-GeM in both the stochastic and spatial space using an example of a forward SDE problem with 38- and 20-dimension stochastic and spatial space, respectively.

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