Machine learning is transforming the way physical systems are modeled, analyzed, and predicted. This session explores AI-driven simulations, data analysis, predictive modeling, and pattern recognition in physics.
Speakers are encouraged to share applications of deep learning, reinforcement learning, and AI algorithms to physical sciences, including material discovery, quantum simulations, and climate modeling.
Attendees will gain insights into state-of-the-art AI tools, learn to handle large-scale datasets, and explore how machine learning enhances predictive capabilities in research. This session bridges computational innovation with physical experimentation, offering practical guidance for integrating AI into scientific workflows.