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arxiv: 2206.02607 · v3 · pith:UUYEKRRBnew · submitted 2022-06-06 · 💻 cs.LG · cs.CE· cs.GR· cs.NA· math.NA· physics.comp-ph

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations

Peter Yichen Chen , Jinxu Xiang , Dong Heon Cho , Yue Chang , G A Pershing , Henrique Teles Maia , Maurizio M. Chiaramonte , Kevin Carlberg
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Eitan Grinspun
This is my paper
classification 💻 cs.LG cs.CEcs.GRcs.NAmath.NAphysics.comp-ph
keywords timescontinuouscromfieldsmethodsmodelingreduced-orderaccuracy
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The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimensionality of discretized vector fields, our continuous reduced-order modeling (CROM) approach builds a low-dimensional embedding of the continuous vector fields themselves, not their discretization. We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations. We validate our approach on an extensive range of PDEs with training data from voxel grids, meshes, and point clouds. Compared to prior discretization-dependent ROM methods, such as linear subspace proper orthogonal decomposition (POD) and nonlinear manifold neural-network-based autoencoders, CROM features higher accuracy, lower memory consumption, dynamically adaptive resolutions, and applicability to any discretization. For equal latent space dimension, CROM exhibits 79$\times$ and 49$\times$ better accuracy, and 39$\times$ and 132$\times$ smaller memory footprint, than POD and autoencoder methods, respectively. Experiments demonstrate 109$\times$ and 89$\times$ wall-clock speedups over unreduced models on CPUs and GPUs, respectively. Videos and codes are available on the project page: https://crom-pde.github.io

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