SmartML performs unsupervised, supervised, and physics/science-informed Machine Learning (ML). SmartML is a module in the SmartTensors ML framework (smarttensors.com).
SmartTensors 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 the 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 the 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"))