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Overview: Stochastic Methods in Scientific Computing: From Foundations to Advanced Techniques introduces the reader to advanced concepts in stochastic modelling, rooted in an intuitive yet rigorous presentation of the underlying mathematical concepts. A particular emphasis is placed on illuminating the underpinning Mathematics, and yet have the practical applications in mind. The reader will find valuable insights into topics ranging from Social Sciences and Particle Physics to modern-day Computer Science with Machine Learning and AI in focus. The book also covers recent specialised techniques for notorious issues in the field of stochastic simulations, providing a valuable reference for advanced readers with an active interest in the field. Alongside the advances in computer hardware and the steady increase of available computer resources, computer simulations for advanced scientific computing have enjoyed an upsurge in interest. Practitioners and scientists realised that many applications have stochastic elements for a number of reasons: the lack of data is compensated by a stochastic model replacing those data; a deterministic description of a system is neither possible nor desirable. The Data Science revolution over the last decade has delivered many practical solutions using algoritms called Machine Learning (ML) or Artificial Intelligence (AI). It has triggered a proliferation of tools for everyday life with tremendous impacts, such as speech recognition, voice, image and video generation and medical and other expert systems to name just a few.
Genre: Non-Fiction > Tech & Devices
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