SmartML is one of the tools in the SmartTensors ML framework (smarttensors.com).
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:
- 2021 R&D100 Award: Information Technologies (IT)
- 2021 R&D100 Bronze Medal: Market Disruptor in Services
After starting Julia, execute:
import Pkg Pkg.add("SmartML")
to access the latest released version.
To utilize the latest code updates (commits), use:
import Pkg Pkg.add(Pkg.PackageSpec(name="SmartML", rev="master"))