Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization
This incremental improvement addresses efficiency issues for researchers and practitioners in fields like physics simulation that rely on expensive data generation.
The paper tackles the problem of high computational costs in generating large multidimensional simulation datasets by introducing tensor decomposition into generative models, reducing model output size and parameters while maintaining data utility.
Producing large complex simulation datasets can often be a time and resource consuming task. Especially when these experiments are very expensive, it is becoming more reasonable to generate synthetic data for downstream tasks. Recently, these methods may include using generative machine learning models such as Generative Adversarial Networks or diffusion models. As these generative models improve efficiency in producing useful data, we introduce an internal tensor decomposition to these generative models to even further reduce costs. More specifically, for multidimensional data, or tensors, we generate the smaller tensor factors instead of the full tensor, in order to significantly reduce the model's output and overall parameters. This reduces the costs of generating complex simulation data, and our experiments show the generated data remains useful. As a result, tensor decomposition has the potential to improve efficiency in generative models, especially when generating multidimensional data, or tensors.