Reduced operator inference for nonlinear partial differential equations

• URL: http://arxiv.org/abs/2102.00083v1
• Date: Fri, 29 Jan 2021 21:50:20 GMT
• Title: Reduced operator inference for nonlinear partial differential equations
• Authors: Elizabeth Qian, Ionut-Gabriel Farcas, and Karen Willcox
• Abstract summary: We present a new machine learning method that learns from data a surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial differential equation (PDE) Our formulation generalizes the Operator Inference method previously developed in [B. Peherstorfer and K. Willcox, Data-driven operator inference for non-intrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, 306] for systems governed by ordinary differential equations.
• Score: 2.389598109913753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new scientific machine learning method that learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial differential equation (PDE), an enabling technology for many computational algorithms used in engineering settings. Our formulation generalizes to the PDE setting the Operator Inference method previously developed in [B. Peherstorfer and K. Willcox, Data-driven operator inference for non-intrusive projection-based model reduction, Computer Methods in Applied Mechanics and Engineering, 306 (2016)] for systems governed by ordinary differential equations. The method brings together two main elements. First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE. Second, supervised machine learning tools are used to infer from data the reduced operators of this physics-informed parametrization. For systems whose governing PDEs contain more general (non-polynomial) nonlinearities, the learned model performance can be improved through the use of lifting variable transformations, which expose polynomial structure in the PDE. The proposed method is demonstrated on a three-dimensional combustion simulation with over 18 million degrees of freedom, for which the learned reduced models achieve accurate predictions with a dimension reduction of six orders of magnitude and model runtime reduction of 5-6 orders of magnitude.

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