SmartTensors

Unsupervised Machine Learning,Supervised Machine Learning,Physics-Informed Machine Learning,Science-Informed Machine Learning,Matrix Factorization,Tensor Factorization,Tensor Networks

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RD100Awards

2021 R&D100 Award: Information Technologies (IT)

2021 R&D100 Bronze Medal: Market Disruptor in Services

SmartTensors

SmartTensors is a general high-performance Unsupervised, Supervised and Physics-Informed Machine Learning and Artificial Intelligence (ML/AI).

SmartTensors includes a series of alternative ML/AI methods / algorithms (NMFk, NTFk, NTTk, SVR, etc.) coupled with constraints (sparsity, nonnegativity, physics, etc.).

SmartTensors awards:

SmartTensors is developed in Julia.

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Nonnegative Matrix Factorization

NMFk is a novel unsupervised ML method based on Matrix Decomposition.

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Nonnegative Tensor Factorization

NTFk is a novel unsupervised ML method based on Tensor Decomposition.

Applications

Research

Resources

SmartTenosrs is open source and available on GitHub

SmartTensors resources include:

  • Codes
  • Scripts
  • Unit tests
  • Test problems
  • Examples
  • Real-world applications and projects (e.g., GeoThermalCloud, ML4Geo, etc.)
  • Jupyter and Pluto notebooks
  • Documentation
  • Videos
  • Tutorials

For more information: info@smarttensors.com, +1 505 473 4150

Velimir V Vesselinov (monty): GitLab GitHub
SmartTensors: Web GitHub Julia
MADS: GitLab GitHub C Python
WELLS: GitLab C Gitlab Julia GitHub
ChroTran: GitHub Gitlab