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Deep neural networks to recover unknown physical parameters from oscillating time series. | LitMetric

AI Article Synopsis

  • Deep neural networks are effective in recognizing patterns in complex data where traditional quantitative descriptions are insufficient, but they often produce abstract representations that are difficult for humans to interpret.
  • In this study, a specialized deep neural network is trained to analyze synthetic oscillating time series by performing signal regression and denoising, achieving results similar to conventional methods without needing initial guesses for parameters.
  • The proposed architecture can be used in various applications in time-series processing, especially when prior knowledge is lacking, allowing for partial knowledge utilization while still uncovering new data insights.

Article Abstract

Deep neural networks are widely used in pattern-recognition tasks for which a human-comprehensible, quantitative description of the data-generating process, cannot be obtained. While doing so, neural networks often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This may be one of the reasons why neural networks are not yet used extensively in physics-experiment signal processing: physicists generally require their analyses to yield quantitative information about the system they study. In this article we use a deep neural network to disentangle components of oscillating time series. To this aim, we design and train the neural network on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings with true latent-parameters initial guesses, in spite of the neural network needing no initial guesses at all. We then explore various applications in which we believe our architecture could prove useful for time-series processing, when prior knowledge is incomplete. As an example, we employ the neural network as a preprocessing tool to inform the least-square fits when initial guesses are unknown. Moreover, we show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106171PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268439PLOS

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