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alexkalitenko125/README.md

Hello there 👋

About Me 👩‍💼

Research info

Research interests: AI/ML methods for everything, numerical modeling of physical processes.

AI is being implemented everywhere because it optimizes human work. The development of AI in physics can be divided into three main categories:

  1. Direct application of AI to solve practical problems (not only in physics).
  2. Physics-inspired AI, which introduces the physical laws into neural networks (physics-informed neural networks, Brain-Inspired Modular Training, and Lagrangian neural networks).
  3. AI for physics - the search for new knowledge, formulas (AI Feynman and AI Poincaré).

Tech Stack

Python PyTorch scikit-learn numpy pandas Matplotlib

Jupyter Notebook Colab GitHub C++

Pinned Loading

  1. NNQS_for_Klein-Gordon-Fock_equation NNQS_for_Klein-Gordon-Fock_equation Public

    NNQS for the Klein--Gordon--Fock equation

    Jupyter Notebook

  2. NNfel NNfel Public

    NN for fast FEL analysis

    Jupyter Notebook 1 1

  3. AI_emotion_register AI_emotion_register Public

    BERT for semantic analysis

    Jupyter Notebook

  4. ML_for_finance ML_for_finance Public

    ML for finance and forecasting

    Jupyter Notebook 1

  5. Grokking_FizzBuzz Grokking_FizzBuzz Public

    Grokking for FizzBuzz

    Jupyter Notebook

  6. AI-Feynman_2_0 AI-Feynman_2_0 Public

    Forked from SJ001/AI-Feynman

    The colab version

    Jupyter Notebook