ARTIFICIAL INTELLIGENCE IN PHYSICS: PREDICTING PHYSICAL PHENOMENA WITH MACHINE LEARNING (KEY IJP************211)

• Piyush Dua

Abstract

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of physics, enabling the development of novel approaches for predicting and analyzing complex physical phenomena. This research explores the application of AI and ML techniques to tackle challenging problems in physics, where traditional analytical methods are often insufficient. By leveraging the power of ML algorithms, researchers can now analyze large datasets, identify patterns, and make accurate predictions about physical systems.The proposed research aims to investigate how AI can be used to interpret complex physical data, model physical systems, and make accurate predictions. Specifically, we will focus on three key areas: (1) classification and regression tasks in particle physics, (2) time-series analysis in astrophysics, and (3) optimization problems in quantum mechanics. We will employ a range of ML techniques, including neural networks, decision trees, and clustering algorithms, to analyze large datasets and make predictions about physical phenomena.The proposed research has the potential to significantly impact our understanding of complex physical systems and shed light on long-standing problems in physics. By harnessing the power of AI and ML, we can accelerate discovery, improve predictive accuracy, and enable the development of new physical models that can be tested experimentally. This research has far-reaching implications for various fields, including particle physics, astrophysics, and quantum mechanics, and could lead to breakthroughs in our understanding of the fundamental laws of nature.This research topic delves into the application of artificial intelligence (AI) and machine learning (ML) techniques to predict and analyze physical phenomena. It explores how AI can be used to interpret complex physical data, model physical systems, and make accurate predictions.