Operator inference for non-intrusive model reduction with quadratic manifolds

Abstract

This paper proposes a novel approach for learning a data-driven quadratic manifold from high-dimensional data, then employing this quadratic manifold to derive efficient physics-based reduced-order models. The key ingredient of the approach is a polynomial mapping between high-dimensional states and a low-dimensional embedding. This mapping consists of two parts: a representation in a linear subspace (computed in this work using the proper orthogonal decomposition) and a quadratic component. The approach can be viewed as a form of data-driven closure modeling, since the quadratic component introduces directions into the approximation that lie in the orthogonal complement of the linear subspace, but without introducing any additional degrees of freedom to the low-dimensional representation. Combining the quadratic manifold approximation with the operator inference method for projection-based model reduction leads to a scalable non-intrusive approach for learning reduced-order models of dynamical systems. Applying the new approach to transport-dominated systems of partial differential equations illustrates the gains in efficiency that can be achieved over approximation in a linear subspace.

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• Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations
• Residual-Based Error Corrector Operator to Enhance Accuracy and Reliability of Neural Operator Surrogates of Nonlinear Variational Boundary-Value Problems
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• A Low Rank Neural Representation of Entropy Solutions
• A parallel implementation of reduced-order modeling of large-scale systems
• Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces
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• Projection-based model-order reduction via graph autoencoders suited for unstructured meshes
• Hyper-reduction-free reduced-order Newton solvers for projection-based model-order reduction of nonlinear dynamical systems
• Model order reduction via Lie groups