Rahul Sengupta

This groundbreaking book, Machine Learning Methods for Scientific Data Compression, delivers an essential exploration into the rapidly evolving field of data reduction for scientific applications. As scientific simulations generate petabytes of data, traditional compression methods falter in maintaining critical fidelity. This work introduces novel machine learning approaches, from advanced autoencoders to generative foundation models, all designed to achieve unprecedented compression ratios while rigorously guaranteeing the accuracy of both primary data and quantities of interest. Dive into comprehensive chapters covering autoencoders, constrained and guaranteed autoencoders, adaptive data reduction, and attention-based hierarchical methods. Discover the power of guaranteed conditional diffusion and the revolutionary potential of foundation models for scientific data. The book culminates in a unified framework for scalable, high-fidelity data reduction, showcasing practical GPU-accelerated pipelines and experimental results across diverse domains like climate modeling, turbulent flow, and plasma physics. This resource provides the tools and insights needed to accelerate scientific discovery by getting smarter faster with data. The book is a must-read for researchers, data scientists, and engineers grappling with the challenges of managing and analyzing colossal scientific datasets in the age of exascale computing.

In an era defined by rapid urbanization and ever-increasing mobility demands, effective transportation management is paramount. This book takes readers on a journey through the intricate web of contemporary transportation systems, offering unparalleled insights into the strategies, technologies, and methodologies shaping the movement of people and goods in urban landscapes. From the fundamental principles of traffic signal dynamics to the cutting-edge applications of machine learning, each chapter of this comprehensive guide unveils essential aspects of modern transportation management systems. Chapter by chapter, readers are immersed in the complexities of traffic signal coordination, corridor management, data-driven decision-making, and the integration of advanced technologies. Closing with chapters on modeling measures of effectiveness and computational signal timing optimization, the guide equips readers with the knowledge and tools needed to navigate the complexities of modern transportation management systems. With insights into traffic data visualization and operational performance measures, this book empowers traffic engineers and administrators to design 21st-century signal policies that optimize mobility, enhance safety, and shape the future of urban transportation.

In an era defined by rapid urbanization and ever-increasing mobility demands, effective transportation management is paramount. This book takes readers on a journey through the intricate web of contemporary transportation systems, offering unparalleled insights into the strategies, technologies, and methodologies shaping the movement of people and goods in urban landscapes. From the fundamental principles of traffic signal dynamics to the cutting-edge applications of machine learning, each chapter of this comprehensive guide unveils essential aspects of modern transportation management systems. Chapter by chapter, readers are immersed in the complexities of traffic signal coordination, corridor management, data-driven decision-making, and the integration of advanced technologies. Closing with chapters on modeling measures of effectiveness and computational signal timing optimization, the guide equips readers with the knowledge and tools needed to navigate the complexities of modern transportation management systems. With insights into traffic data visualization and operational performance measures, this book empowers traffic engineers and administrators to design 21st-century signal policies that optimize mobility, enhance safety, and shape the future of urban transportation.