I am currently working on developing new computational methods for quantum physics, including the application of PINNs (physics-informed neural networks) and NNQS (neural network quantum states).
Research interests: AI/ML methods for everything, numerical modeling of physical processes and teaching.
AI is being implemented everywhere because it optimizes human work. The development of AI in physics can be divided into three main categories:
- Direct application of AI to solve practical problems (not only in physics).
- Physics-inspired AI, which introduces the physical laws into neural networks (physics-informed neural networks, Brain-Inspired Modular Training, and Lagrangian neural networks).
- AI for physics - the search for new knowledge, formulas (AI Feynman and AI Poincaré).
- Application of machine learning techniques for theoretical research in physics, including MLP, MTL, RL, PINNS, NNQS and etc.
- Development of methods for teaching physics, mathematics, and machine learning at different levels, from middle school to university.
- Teaching physics to university students, including international students from China, Vietnam, the UAE, Saudi Arabia and so on.
- Adapting machine learning algorithms for financial analytics in my free time.
