Author SmartTensors
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Updated Last
1 Year Ago
Started In
May 2022


SmartML is one of the tools in the SmartTensors ML framework (


SmartML applied unsupervised and supervised machine learning methodology that allows for automatic identification of the optimal number of features (signals/signatures) present in the data.

SmartML can be applied to perform:

  • Feature extraction (FE)
  • Blind source separation (BSS)
  • Detection of disruptions / anomalies
  • Image recognition
  • Text mining
  • Data classification
  • Separation (deconstruction) of co-occurring (physics) processes
  • Discovery of unknown dependencies and phenomena
  • Development of reduced-order/surrogate models
  • Identification of dependencies between model inputs and outputs
  • Guiding the development of physics models representing the ML analyzed data
  • Blind predictions
  • Optimization of data acquisition (optimal experimental design)
  • Labeling of datasets for supervised ML analyses

SmartML provides high-performance computing capabilities to solve problems with Shared and Distributed Arrays in parallel. The parallelization allows for utilization of multi-core / multi-processor environments. GPU and TPU accelerations are available through existing Julia packages.

SmartML provides advanced tools for data visualization, pre- and post-processing. These tools substantially facilitate utilization of the package in various real-world applications.

SmartML methodology and applications are discussed in the research papers and presentations listed below.

SmartML is demonstrated with a series of examples and test problems provided here.


SmartTensors and SmartML were recently awarded:



After starting Julia, execute:

import Pkg

to access the latest released version.

To utilize the latest code updates (commits), use:

import Pkg
Pkg.add(Pkg.PackageSpec(name="SmartML", rev="master"))