Deep Learning and Physics

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

• Chapter 1: Forewords: Machine learning and physics.- Part I Physical view of deep learning.- Chapter 2: Introduction to machine learning.- Chapter 3: Basics of neural networks.- Chapter 4: Advanced neural networks.- Chapter 5: Sampling.- Chapter 6: Unsupervised deep learning.- Part II Applications to physics.- Chapter 7: Inverse problems in physics.- Chapter 8: Detection of phase transition by machines.- Chapter 9: Dynamical systems and neural networks.- Chapter 10: Spinglass and neural networks.- Chapter 11: Quantum manybody systems, tensor networks and neural networks.- Chapter 12: Application to superstring theory.- Chapter 13: Epilogue.- Bibliography.- Index.

“The book has the feel of a graduate thesis. It could be quite useful to a researcher investigating the relationship between ANNs and dynamical physical systems.” (Anoop Malaviya, Computing Reviews, February 16, 2023)