Institute of Solid State Physics Laboratory of Materials Morphology and Structure Investigations
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Artificial Neural Network


Artificial Neural Network (ANN) is a type of machine learning algorithm or an information processing model consisting of elements, called artificial neurons, which are interconnected into several layers (one input layer, one or more hidden layers, and one output layer). The complex relationships between inputs and outputs are modeled during the training process using a large set of reference (experimental or theoretical) data. When trained properly, the ANN can fast generate the result based on the input data.

In our laboratory, we develop the ANNs methodology to perform the analysis of X-ray absorption data.




Example: "Neural network approach for characterizing structural transformations by x-ray absorption fine structure spectroscopy"
    (from J. Timoshenko, A. Anspoks, A. Cintins, A. Kuzmin, J. Purans, A.I. Frenkel, Phys. Rev. Lett. 120 (2018) 225502:1-6.)

Scheme of the EXAFS spectrum analysis using the artificial neural network


USEFUL LINKS

  • Artificial Neural Network (ANN) (Wikipedia).
  • Neural Networks and Deep Learning - a free online book

  • TensorFlow - an open source machine learning library for research and production.
  • Caffe - a deep learning framework made with expression, speed, and modularity in mind.
  • MXNet - a scalable deep learning framework.
  • FANN - Fast Artificial Neural Network Library.