Efficient rare event sampling with unsupervised normalizing flows

• Main Authors: Asghar, Solomon, Pei, Qing-Xiang, Volpe, Giorgio, Ni, Ran
• Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
• Format: Article
• Language: English
• Published: 2025
• Subjects: Computer and Information Science; Learning frameworks; Machine-learning
• Online Access: https://hdl.handle.net/10356/182399
• Institution: Nanyang Technological University

From physics and biology to seismology and economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as rare events, the study of which is essential for understanding and controlling the properties of these systems. Classical co...