Abstract
The proper orthogonal decomposition (POD) method has been widely used to construct efficient numerical surrogate models for computationally intensive applications in control and optimization. An inherent challenge with this method is that POD basis generation can be computationally expensive due to the huge size of the input snapshot data obtained from typical high-fidelity, large-scale dynamic system simulations. However, if the process can be distributed into much smaller tasks over multiple processors in parallel, computational time can be drastically reduced. In this paper, we put forth a novel partitioned method for generating the POD basis from snapshot data. This method preserves the distributed nature of the data and takes advantage of parallelism for computation. Additionally, it greatly reduces subtask communication volume. Two numerical examples are presented that demonstrate the effectiveness of the new method.
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