A tutorial-style paper that details and demonstrates a fully distributed implementation of Operator Inference for building reduced-order models from datasets too large for a single computer.
Learning Nonlinear Reduced Models from Data with Operator Inference,
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A parallel implementation of reduced-order modeling of large-scale systems
A tutorial-style paper that details and demonstrates a fully distributed implementation of Operator Inference for building reduced-order models from datasets too large for a single computer.