Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations

Abstract

An integration of machine learning (ML) with high-performance computing (HPC) is radically transforming research in the scientific field. ML algorithms can span large-scale simulations and predict a great deal in materials science, climate modeling, and drug discovery by optimizing resource allocation and finding novel insights. HPC has accelerated complex data-sets processing, and ML models allow automation in pattern recognition and advanced analytics. The synergy of these technologies accelerates the experimentation rate, shortens time-to-discovery, and improves the scalability to tackle complex scientific challenges. It explores different key methodologies, case studies, and directions with future impacts that illustrate how advances in ML and HPC can revolutionize scientific innovation across disciplines. It demonstrates improved problem-solving capacity and accuracy in prediction as it addresses some of the most profound global challenges.