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High Performance Computing
This book constitutes the proceedings of the 9th Latin American Conference on High Performance Computing, CARLA 2022, held in Porto Alegre, Brazil, in September 2022. The 16 full papers presented in this volume were carefully reviewed and selected from 56 submissions. CARLA, the Latin American High Performance Computing Conference, is an international academic meeting aimed at providing a forum to foster the growth and strength of the High Performance Computing (HPC) community in Latin America and the Caribbean through the exchange and dissemination of new ideas, techniques, and research in HPC and its application areas.
Machine Learning for Materials Discovery
Zusammenfassung: Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect--each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials
After the Glory
"Shaffer chronicles the postwar transition of black veterans from the Union army, as well as their subsequent life patterns, political involvement, family and marital life, experiences with social welfare, comradeship with other veterans, and memories of the war itself. He draws on such sources as Civil War pension records to fashion a collective biography - a social history of both ordinary and notable lives - resurrecting the words and memories of many black veterans to provide an intimate view of their lives and struggles."--BOOK JACKET.
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems. Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-L...
Advances in Numerical Methods for Hyperbolic Balance Laws and Related Problems
A broad range of phenomena in science and technology can be described by non-linear partial differential equations characterized by systems of conservation laws with source terms. Well known examples are hyperbolic systems with source terms, kinetic equations, and convection-reaction-diffusion equations. This book collects research advances in numerical methods for hyperbolic balance laws and kinetic equations together with related modelling aspects. All the contributions are based on the talks of the speakers of the Young Researchers’ Conference “Numerical Aspects of Hyperbolic Balance Laws and Related Problems”, hosted at the University of Verona, Italy, in December 2021.
