• Title Physics-Based Deep Learning
• Author(s) Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, Kiwon Um
• Publisher: PhysicsBasedDeepLearning.org
• Hardcover/Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations, focuses on physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, training algorithms tailored to physics problems, as well as reinforcement learning and uncertainty modeling.

• N/A

• Amazon

• Deep Learning and Neural Networks
• Physics, Computational Physics, and Mathematical Physics
• Machine Learning
• Data Science
• Data Analysis and Data Mining

• Physics-Based Deep Learning (Nils Thuerey, et al.)

• Deep Learning (Ian Goodfellow, et al)

  Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

• Hyperparameter Tuning for Deep Learning: A Practical Guide

  This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

• Deep Learning with Python, 2nd Edition (Francois Chollet)

  This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.

• Dive into Deep Learning (Aston Zhang, et al.)

  This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• Computational Physics: An Introduction (Franz J. Vesely)

  A concise introduction to the methods and algorithms used in computational physics, clear in its presentation, useful for those beginning more advanced work in the field. Sample programs are be written in JAVA and are accompanied by short explanations.

• Computational Physics and Scientific Computing: C++ or Fortran

  This book is an introduction to the computational methods used in physics, but also in other scientific fields. Both C++ or Fortran are used for programming the core programs and data analysis is performed using the powerful tools of the Gnu/Linux environment.

• Computational Physics with Python (Eric Ayars)

  This book provides an unusually broad survey of the topics of modern computational physics. Its philosophy is rooted in learning by doing, with new scientific materials as well as with the Python programming language.

• Computational Physics with Python (Mark Newman)

  A complete introduction to the field of computational physics, with examples and exercises in the Python programming language. It explains the fundamentals of computational physics and describes in simple terms the techniques that every physicist should know,.