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Wiley-VCH - Physics
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Computation, Optimization, and Machine Learning in Seismology
A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: * Mathematical foundations and key equations for computational seismology * Essential theories, including wave propagation and elastic wave theory * Processing, mapping, and interpretation of prestack data * Model-based optimization and artificial intelligence methods * Applications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problems * Exercises applying the main concepts of each chapter [416 Pages, Softcover]
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Principles of Nanoscience and Molecular Engineering
Introduces students to nanoscale principles in molecular engineering, provides hands-on experience and stresses the interdisciplinary nature of this field. [400 Pages, Hardcover]
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Charged Particle Beam Physics
This scientifically up-to-date book lays the foundation for modeling, designing and implementing accelerator device components, using modern approaches such as the transfer-matrix method and numerical simulation using beam optics codes. [288 Pages, Hardcover]
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Energetics of the Climate System
[352 Pages, Hardcover]
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Introduction to Classical Electrodynamics, Volume 2
[448 Pages, Hardcover]
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