The Net Advance of Physics

The Net Advance of Physics:
NEURAL NETWORKS

General: Textbooks: Gurney 96/06;
- Artificial Neural Networks by Bernhard Mehlig [2019/01] 176 pp.
- Introduction to Engineering Mathematics and Analysis: Modeling Physical Systems Using the Language of Mathematics by B. D. Wood [2023/05] Undergraduate textbook, 474 pp.
General:
- Biology-Derived Algorithms in Engineering Optimization by Xin-She Yang [2005]
- From Neuron to Neural Networks dynamics by B. Cessac and M. Samuelides [2006/09]
- The Physics of Living Neural Networks by Jean-Pierre Eckmann et al. [Physics Reports 449, 54 (2007)]
- Complex and Adaptive Dynamical Systems: A Primer by Claudius Gros [Springer, 2008]
- A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks by Marcus Kaiser [2011/05]
- A Tutorial on Deep Neural Networks for Intelligent Systems by Juan C. Cuevas-Tello et al. [2016/03]
- A Survey of Neuromorphic Computing and Neural Networks in Hardware by Catherine D. Schuman et al. [2017/05]
- Introduction to Intelligent Computing, Unit 1 by Isa Inuwa-Dutse [2017/11]
- Apuntes de redes neuronales artificiales by Juan C. Cuevas-Tello [2018/06]
- Neural Network Models and Deep Learning: A Primer for Biologists by Nikolaus Kriegeskorte and Tal Golan [2019/02]
- Physicists' Journeys Through the AI World: A Topical Review by Imad Alhousseini et al. [2019/05]
- Distributed training and optimization of neural networks by J.-R. Vlimant and JQ. Yin [2020/12]
- Topological distances and losses for brain networks by M. K. Chung et al. [2021/02]
- Neural-network architectures by E. Herberg [2023/04]
- Neural networks: From the perceptron to deep nets by M. Gabrié et al. [2023/04]
- How to train a neural-network potential by A. M. Tokita and J. Behler [2023/08]
- An introduction to neural networks for physicists by G. Café de Miranda et al. [2025/05]
Type: BAYESIAN:
- Bayesian neural networks by T. Charnock et al. [2020/06]
- A primer on Bayesian neural networks: Review and debates by J. Arbel et al. [2023/09]
Type: BIOLOGICAL:
- Biology-Derived Algorithms in Engineering Optimization by Xin-She Yang [2005]
- From Neuron to Neural Networks dynamics by B. Cessac and M. Samuelides [2006/09]
- The Physics of Living Neural Networks by Jean-Pierre Eckmann et al. [Physics Reports 449, 54 (2007)]
- The importance of the whole: topological data analysis for the network neuroscientist by Ann E. Sizemore et al. [2018/06]
- Network models in neuroscience by Danelle S. Bassett [2018/07]
- The physics of brain network structure, function, and control by Christopher W. Lynn and Danielle S. Bassett [2018/09]
- Interfacing Graphene-Based Materials with Neural Cells by Mattia Bramini et al. [2019/03]
- Deep learning for cognitive neuroscience by Katherine R. Storrs and Nikolaus Kriegeskorte [2019/03]
- A Review on Understanding Brain, Memory Retention and Recall Processes Using EEG and fMRI Techniques by Qazi Emad-Ul-Haq et al. [2019/05]
- Brain Network Similarity: Methods and Applications by Ahmad Mheich et al. [2019/08]
- Quantum Effects in the Brain: A Review by Betony Adams and Francesco Petruccione [2019/10]
- Topological distances and losses for brain networks by M. K. Chung et al. [2021/02]
- Statistical models of complex brain networks by V. Dichio and F. De Vico Fallani [2022/09]
- Towards principles of brain network organization and function by S. Kulkarni and D. S. Bassett [2024/08]
Type: BIOLOGICAL: SINGLE NEURONS:
- Low-dimensional models of single neurons: A review by U. Chialva et al. [2022/09]
Type: CLIFFORD:
- Introduction to Clifford's Geometric Algebra by Eckhard Hitzer [Journal of the Society of Instrument and Control Engineers 51, 338 (2012)]
Type: CONVOLUTIONAL:
- Recent Advances in Convolutional Neural Networks by Jiuxiang Gu et al. [2015/12]
Type: EQUIVARIANT:
- What is an equivariant neural network? by L.-H. Lim and B. J. Nelson [2022/05]
Type: GRAPH:
- Graph Neural Networks: A Review of Methods and Applications by Jie Zhou et al. [2018/12]
Type: MANY BODY:
- Neural networks in quantum many-body physics: A hands-on tutorial by J. Carrasquilla and G. Torlai [2021/01]
Type: NEUROMORPHIC SPIKING:
- Neuromorphic Spiking Neural Neural Networks for Temporal Learning by Doo Seok Jeong [2018/09]
Type: PHOTONIC:
- Principles of Neuromorphic Photonics by Bhavin J. Shastri et al. [2018/01]
- A comprehensive survey on nanophotonic neural networks by K. Demertzis et al. [2021/11]
Type: QUANTUM:
- Quantum neural networks: Concepts, applications, and challenges by Y. Kwak et al. [2021/08]
Type: RECURRENT:
- Random Recurrent Neural Networks Dynamics by M. Samuelides and B. Cessac [2006/12]
- Recent Advances in Recurrent Neural Networks by Hojjat Salehinehad et al. [2018/01]
Type: SELF-ORGANISING MAP:
- Advances in Self Organising Maps by Marie Cottrell and Michel Verleysen [Neural Networks 19, 721 (2006)]
Type: TURING:
- A Review of Neural Turing Machine by Soroor Malekmohammadi Faradonbeh and Faramarz Safi-Esfahani [2019/04]
Aspects: ATTRACTORS: Parisi 94/12;
Aspects: MEAN-FIELD REDUCTIONS:
- Understanding the Dynamics of Biological and Neural Oscillator Networks Through Mean-Field Reductions: A Review by Christian Bick et al. [2019/02]
Re: DATA COMPRESSION:
- An introduction to neural data compression by Y. Yang et al. [2022/02]
Re: DYNAMICAL SYSTEMS:
- Neural Networks as dynamical systems by Bruno Cessac [2009/01]
- Control of Dynamics in Brain Networks by Evelyn Tang and Danielle S. Bassett [2017/01]
Re: GEOPHYSICS:
- An introduction to distributed training of deep neural networks for segmentation tasks with large seismic data sets by C. Birnie et al. [2021/02]
- A short introduction to neural networks and their application to earth and materials science by D. Fanelli et al. [2024/08]
Re: INFORMATION THEORY:
- Applications of Information Theory to Analysis of Neural Data by Simon R. Schultz et al. [2015/01]
- Estimating Information-Theoretic Quantities by Robin A. A. Ince et al. [2015/01]
Re: MATERIALS SCIENCE:
- A short introduction to neural networks and their application to earth and materials science by D. Fanelli et al. [2024/08]
Re: MECHANICS:
- A review on turbulent and vortical flow analyses via complex networks by G. Iacobello et al. [2020/11]
Re: MOLECULES:
- Graph neural networks for molecules by Y.-Y. Wang et al. [2022/09]
Re: NANOPARTICLES:
- Magnetic nanoparticles for neural engineering by G. F. Goya and V. Raffa [2024/02]
Re: PARTIAL DIFFERENTIAL EQUATIONS:
- Three ways to solve partial differential equations with neural networks by J. Blechschmidt and O. G. Ernst [2021/02]
- A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics by H. Baty [2024/03]
Re: PERCOLATION:
- The Physics of Living Neural Networks by Jean-Pierre Eckmann et al. [Physics Reports 449, 54 (2007)]
Re: TOPOLOGY:
- The importance of the whole: topological data analysis for the network neuroscientist by Ann E. Sizemore et al. [2018/06]

THE NET ADVANCE OF PHYSICS