The Net Advance of Physics:

The Net Advance of Physics:
MACHINE LEARNING

COMPUTATIONAL METHODS: MACHINE/DEEP LEARNING

General:
- Deep learning: Technical introduction by Thomas Epelbaum [2017/09]
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone [2017/09]
- Contemporary machine learning: a guide for practitioners in the physical sciences by Brian K. Spears [2017/12]
- Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham and Desmond J. Higham [2018/01]
- The Matrix Calculus You Need For Deep Learning by Terence Parr and Jeremy Howard [2018/02]
- A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta et al. [2018/03] 119 pp.
- Review of Deep Learning by Rong Zhang et al. [2018/04] In Chinese.
- A Gentle Introduction to Supervised Machine Learning by Alexander Jung [2018/05]
- Como funciona o Deep Learning by Moacir Antonelli Ponti and Gabriel B. Paranhos da Costa [2018/06] In Portuguese.
- Deep Reinforcement Learning: An Overview by Seyed Sajad Mousavi et al. [2018/06]
- Deep Learning and its Application to LHC Physics by Dan Guest et al. [2018/06]
- Deep Learning by Nicholas G. Polson and Vadim O. Sokolov [2018/07]
- Recent Advances in Deep Learning: An Overview by Matiur Rahman Minar and Jibon Naher [2018/07]
- A Very Brief Introduction to Machine Learning With Applications to Communication Systems by Osvaldo Simeone [2018/08]
- Deep Learning: Computational Aspects by Nicholas Polson and Vadim Sokolov [2018/08]
- Deep Reinforcement Learning by Yuxi Li [2018/10]
- Deep Learning on Graphs: A Survey by Ziwei Zhang et al. [2018/12]
- Artificial Neural Networks by Bernhard Mehlig [2019/01] 176 pp.
- Neural Network Models and Deep Learning: A Primer for Biologists by Nikolaus Kriegeskorte and Tal Golan [2019/02]
- Machine Learning Methods Economists Should Know About by Susan Athey and Guido Imbens [2019/03]
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Eigenvalue and Generalized Eigenvalue Problems: Tutorial by Benyamin Ghojogh et al. [2019/03] with some examples taken from machine-learning theory after a more general algebraic discussion.
- A Selective Overview of Deep Learning by Jianqing Fan et al. [2019/04]
- The Many Faces of Deep Learning by Raul Vicente [2019/08]
- A modern introduction to online learning by F. Orabona [2019/12]
- Reinforcement learning for combinatorial optimization: a survey by N. Mazyavkina et al. [2020/03]
- Machine learning and computational mathematics by W. E [2020/09]
- Machine learning and quantum devices by F. Marquardt [2021/01]
- Introduction to machine learning for the sciences by T. Neupert et al. [2021/02]
- The physics of machine learning: An intuitive introduction for the physical scientist by S. Alexander et al. [2021/12]
- Machine learning for computational science and engineering: A brief introduction and some critical questions by C. Kadapa [2021/12]
- The mathematics of artificial intelligence by G. Kutyniok [2022/03]
- Deep learning and computational physics: Lecture notes by D. Ray et al. [2023/01]
- Recent advances in optimal transport for machine learning by E. F. Montesuma et al. [2023/06]
- Machine learning in physics: A short guide by F. A. Rodrigues [2023/10]
- A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models by N. Suh and G. Cheng [2024/01]
- Matern correlation: A panoramic primer by X.-Q. Chen [2024/04]
- Reinforcement learning by S. Yatawatta [2024/05]
- Mathematical theory of deep learning by P. Petersen and J. Zech [2024/07] 267 pp.
- TASI Lectures on Physics for Machine Learning by J. Halverson [2024/08]
- Interpretable machine learning in physics: A review by S. J. Wetzel et al. [2025/03]
Type: QUANTUM:
- An Introduction to Quantum Machine Learning by M. Schuld et al. [2014/09]
- A Survey of Quantum Learning Theory by Srinavasan Arunachalam and Ronald de Wolf [2017/01]
- Quantum Machine Learning for Data Scientists by Dawid Kopczyk [2018/04]
- Advances in quantum deep learning: An overview by S. Garg and G. Ramakrishnan [2020/05]
- Machine learning and quantum devices by F. Marquardt [2021/01]
- New trends in quantum machine learning by L. Buffoni and F. Caruso [2021/08]
- Systematic literature review: Quantum machine learning and its applications by D. Peral García et al. [2022/01]
- From quantum graph computing to quantum graph learning: A survey by Y.-H. Tang et al. [2022/02]
- Challenges and opportunities in quantum machine learning by M. Cerezo et al. [2023/03]
- A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead by K. Zaman et al.y [2023/10]
- A quick introduction to quantum machine learning for non-practitioners by E. N. Evans et al. [2024/02]
- Reinforcement learning by S. Yatawatta [2024/05]
- A brief review of quantum machine learning for financial services by M. Doosti et al. [2024/07]
- Quantum artificial intelligence: A brief survey by M. Klusch et al. [2024/08]
- An introduction to quantum reinforcement learning by S. Y.-C. Chen [2024/09]
Type: TOPOLOGICAL:
- Topological data analysis and machine learning by D. Leykam and D. G. Angelakis [2022/06]
- Topological methods in machine learning: A tutorial for practitioners by B. Coskunuzer and C. G. Akçora [2024/09]
Aspects: FEATURE IMPORTANCE:
- A guide to feature-importance methods for scientific inference by F. K. Ewald et al. [2024/04]
Re: ACOUSTICS:
- Machine Learning in Acoustics: A Review by Michael J. Bianco et al. [2019/05]
Re: ASTROPHYSICS:
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Machine Learning in Astronomy: A Practical Overview by Dayla Baron [2019/04]
- Applications of AI in astronomy by S. G. Djorgovski et al. [2022/12]
- Introduction of machine learning for astronomy by Y. Wang et al. [2023/02]
- Analyzing astronomical data with machine-learning techniques by M. H. Z. Haghighi [2023/02]
- An astronomer's guide to machine learning by S. A. Webb and S. R. Goode [2023/04]
- Machine learning in solar physics by A. A. Ramos et al. [2023/06] 100 pp.
- Machine learning in stellar astronomy: Progress up to 2024 by G.-P. Li et al. [2025/02]
Re: BIOLOGY:
- Deep Learning in Bioinformatics: Introduction, Application, and Perspective in Big Data Era by Yu Li et al. [2019/03]
- Deep learning for cognitive neuroscience by Katherine R. Storrs and Nikolaus Kriegeskorte [2019/03]
- Machine Learning in Acoustics: A Review by Michael J. Bianco et al. [2019/05]
- Noise and artifact reduction for MRI using deep learning by D. Tamada [2020/02]
- Deep learning in biomedical optics by L. Tian et al. [2021/05]
- Known operator learning and hybrid machine learning in medical imaging: A review of the past, the present and the future by A. Maier et al. [2021/08]
- Recent advancements in artificial intelligence in particle therapy by H. Peng et al. [2022/11]
- Deep learning in MRI-guided radiation therapy: A systematic review by Z. Eidex et al. [2023/03]
Re: CALABI-YAU MAINFOLDS:
- The Calabi-Yau Landscape: From Geometry to Physics to Machine Learning by Yang-Hui He [2018/12] 199 pp.
Re: CONDENSED-MATTER PHYSICS:
- Machine Learning for Condensed Matter Physics by E. A. Bedolla-Montiel et al. [2020/05]
Re: DISCRETE DISTRIBUTIONS:
- A short note on learning discrete distributions by C. L. Canonne [2020/02]
Re: DYNAMICAL SYSTEMS:
- Differentiable Programming and Its Applications to Dynamical Systems by A. Hernández and J. M. Amigó [2019/12]
Re: ENTROPY:
- Applications of entropy in data analysis and machine learning by S. A. Sepúlveda Fontaine and J. M. Amigó [2025/03]
Re: GENOMICS:
- Deep Learning for Genomics: A Concise Overview by Tianwei Yue and Haohan Wang [2018/02]
Re: GEOSCIENCES:
- Deep learning in remote sensing: a review by Xiao Xiang Zhu et al. [2017/10]
- Machine Learning in Acoustics: A Review by Michael J. Bianco et al. [2019/05]
- Seventy years of machine learning in geoscience in review by J. S. Dramsch [2020/06]
- A comprehensive review of deep learning applications in hydrology and water resources by M. Sit et al. [2020/07]
Re: HYDRODYNAMICS:
- Machine Learning for Fluid Mechanics by Steven Brunton et al. [2019/05]
- A Review on Deep Reinforcement Learning for Fluid Mechanics by Paul Garnier et al. [2019/08]
- A review on deep-reinforcement-learning for fluid mechanics: An update by J. Viquerat et al. [2021/07]
Re: HIGH-ENERGY PHYSICS:
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Machine and Deep Learning Applications in Particle Physics by D. Bourikov [2019/12]
- Dealing with nuisance parameters using machine learning in high energy physics: A review by T. Dorigo and P. de Castro [2020/07]
- A Review on Machine Learning for Neutrino Experiments by F. Psihas et al. [2020/08]
- Modern machine learning and particle physics by M. D. Schwartz [2021/03]
- High-energy-physics image classification: A survey of jet applications by H. Kheddar et al. [2024/03]
Re: MATERIALS SCIENCE:
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Machine Learning in Materials Modeling: Fundamentals and the Opportunities in 2D Materials by S. Das et al. [2020/01]
- Computer vision and machine learning for microstructural characterization and analysis by E. A. Holm et al. [2020/05]
- Opportunities and challenges for machine learning in materials science by D. Morgan and R. Jacobs [2020/06]
- Advances in machine learning in materials science: Ideas and techniques by S. S. Chong et al. [2023/07]
- AI-driven materials design: A mini-review by M.-Y. Chang et al. [2025/02]
- A practical guide to machine learning interatomic potentials: Status and future by R. Jacobs et al. [2025/03]
Re: METEOROLOGY:
- A review of radar-based nowcasting of precipitation and applicable machine learning techniques by R. Prudden et al. [2020/05]
Re: MICROSCOPY:
- Review: Deep learning in electron microscopy by J. M. Ede [2020/09]
- Deep learning in biomedical optics by L. Tian et al. [2021/05]
- Advancing biological super-resolution microscopy through deep learning: A brief review by T.-J. Yang et al. [2021/06]
- Digital staining in optical microscopy using deep learning by L. Kreiss et al. [2023/03]
Re: MOLECULAR DYNAMICS:
- Enhanced sampling with machine learning: A review by S. Mehdi et al. [2023/06]
Re: MOLECULAR PHYSICS:
- Advances of Machine Learning in Molecular Modeling and Simulation by Mojtaba Haghighatlari and Johannes Hachmann [2019/02]
- Deep Learning for Molecular Generation and Optimization: A Review of the State of the Art by Daniel C. Elton et al. [2019/03]
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Machine Learning for Molecular Simulation by F. Noé et al. [2019/11]
- Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy by D. Chen et al. [2020/01]
- Machine learning at the atomic scale by F. Musil and M. Ceriotti [2020/12]
Re: NUCLEAR PHYSICS:
- Artificial intelligence and machine learning in nuclear physics by A. Boehnlein et al. [2021/12]
- Machine learning in nuclear-materials research by D. Morgan et al. [2022/11]
Re: OPTIMISATION:
- A Survey of Optimization Methods from a Machine Learning Perspective by Shiliang Sung et al. [2019/06]
- Lecture Notes: Optimization for Machine Learning by Elad Hazan [2019/09]
- Optimization for Deep Learning: Theory and Algorithms by R. Sun [2019/12]
- Reinforcement learning for combinatorial optimization: a survey by N. Mazyavkina et al. [2020/03]
- Learning To Optimize (L2O): A tutorial for continuous and mixed-integer optimization by X.-H. Chen et al. [2024/05]
- A survey of optimization methods for training DL models: Theoretical perspectives on convergence and generalization by J. Wang and A. Choromanska [2025/01]
Re: PARTIAL DIFFERENTIAL EQUATIONS:
- An overview on deep-learning-based approximation methods for partial differential equations by C. Beck et al. [2020/12]
Re: PHOTONICS:
- Photonics for artificial intelligence and neuromorphic computing by B. J. Shastri et al. [2020/11]
- A newcomer's guide to deep learning for inverse design in nano-photonics by A. Khaireh-Walieh et al. [2023/07]
Re: QUANTUM MECHANICS:
- The fundamentals of quantum machine learning by Bing Huang et al. [2018/07] meaning application of machine learning to quantum mechanics.
- Machine Learning and the Physical Sciences by Giuseppe Carleo et al. [2019/03]
- Machine learning meets quantum foundations: A brief survey by K. Bharti et al. [2020/03]
- Modern applications of machine learning in quantum sciences by A. Dawid et al. [2022/04]
- Learning quantum systems by V. Gebhart et al. [2022/07]
Re: SOCIAL SCIENCES:
- Machine Learning Methods Economists Should Know About by Susan Athey and Guido Imbens [2019/03]
- Advances in Machine Learning for the Behavioral Sciences by T. Kliegr et al. [2019/11]
- A brief review of quantum machine learning for financial services by M. Doosti et al. [2024/07]
Re: THERMAL PHYSICS:
- Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer by Y. Suh et al. [2023/09]
Re: ULTRASOUND:
- Artificial intelligence in quantitative ultrasound imaging: A review by B. Zhou et al. [2020/03]
Re: UNCERTAINTY:
- Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction by Eyke Hüllermeier and Willem Waegeman [2019/10]

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THE NET ADVANCE OF PHYSICS