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Awesome-Machine-learning-for-discovery-of-physical-laws
A curated list of awesome resources on using machine learning and data science for discovery of physical laws
https://github.com/usccolumbia/Awesome-Machine-learning-for-discovery-of-physical-laws
- awesome-computer-vision
- Machine Learning and Evolution Laboratory at University of South Carolina
- Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery network
- AI daily
- Forbes
- article
- link
- Harvard link
- Talk
- Advancing Fusion with Machine Learning workshop report
- theverge
- AI could be the perfect tool for exploring the Universe
- Tackling Climate Change with Machine Learning ICLR2020 workshop papers
- A machine-learning revolution for physics and materials science...
- Machine learning meets quantum physics
- Machine learning versus physics-based modeling
- AI Copernicus ‘discovers’ that Earth orbits the Sun
- AI Teaches Itself Laws of Physics
- Link
- link - Marian, and Max Tegmark.
- link
- Discovering Physical Concepts with Neural Networks
- arxiv
- arxiv
- link
- `arxiv`
- `arxiv`
- `arxiv`
- `pdf`
- `arxiv`
- `arxiv`
- link
- Physics Based Deep Learning
- link
- link - i-from-passive-to-active-generative-and-reinforcement-learning-with-physics/?tab=schedule)
- github Awesome deep learning resources
- `arxiv`
- `arxiv`
- `pdf`
- Blog
- `pdf`
- `arxiv` - Designed-Graph-Convolutions)
- `arxiv`
- `arxiv`
- `arxiv`
- `arxiv` - Learning-on-Relational-Databases-with-GNNs)
- `arxiv`
- `arxiv`
- `arxiv`
- Link - programs/machine-learning-for-physics-and-the-physics-of-learning/?tab=seminar-series)
- link
- IPAM link
- link
- link
- link - ii-interpretable-learning-in-physical-sciences/?tab=schedule)
- link
- videos - iv-deep-geometric-learning-of-big-data-and-applications/)
- ML4science workshop
- Machine Learning: Science and Technology
- AI Feynman symbolic regression package
- Eureka commercial
- Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
- S. S. Sahoo, C. H. Lampert, and G. Martius, “Learning Equations for Extrapolation and Control,” jun 2018. [Online
- fast symbolic regression
- Physics-Informed Learning Machines
- Resources for students - Frédo Durand (MIT)
- Advice for Graduate Students - Aaron Hertzmann (Adobe Research)
- Graduate Skills Seminars - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
- Research Skills - Simon Peyton Jones (Microsoft Research)
- Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)
- Write Good Papers - Frédo Durand (MIT)
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
- How to write a good CVPR submission - William T. Freeman (MIT)
- How to write a great research paper - Simon Peyton Jones (Microsoft Research)
- How to write a SIGGRAPH paper - SIGGRAPH ASIA 2011 Course
- Writing Research Papers - Aaron Hertzmann (Adobe Research)
- How to Write a Paper for SIGGRAPH - Jim Blinn
- How to Get Your SIGGRAPH Paper Rejected - Jim Kajiya (Microsoft Research)
- How to Write a Great Paper - Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)
- How to have a paper get into SIGGRAPH? - Takeo Igarashi (The University of Tokyo)
- Good Writing - Marc H. Raibert (Boston Dynamics, Inc.)
- How to Write a Computer Vision Paper - Derek Hoiem (UIUC)
- Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)
- Giving a Research Talk - Frédo Durand (MIT)
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- Designing conference posters - Colin Purrington
- Physics-Based-Deep-Learning
- How to do research - William T. Freeman (MIT)
- You and Your Research - Richard Hamming
- Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information - Yi Ma (UIUC)
- Seven Warning Signs of Bogus Science - Robert L. Park
- Five Principles for Choosing Research Problems in Computer Graphics - Thomas Funkhouser (Cornell University)
- How To Do Research In the MIT AI Lab - David Chapman (MIT)
- Recent Advances in Computer Vision - Ming-Hsuan Yang (UC Merced)
- How to Come Up with Research Ideas in Computer Vision? - Jia-Bin Huang (UIUC)
- How to Read Academic Papers - Jia-Bin Huang (UIUC)
- Time Management - Randy Pausch (CMU)
- Learn OpenCV - Satya Mallick
- Tombone's Computer Vision Blog - Tomasz Malisiewicz
- Computer vision for dummies - Vincent Spruyt
- Andrej Karpathy blog - Andrej Karpathy
- AI Shack - Utkarsh Sinha
- Computer Vision Talks - Eugene Khvedchenya
- Computer Vision Basics with Python Keras and OpenCV - Jason Chin (University of Western Ontario)
- The Computer Vision Industry - David Lowe
- German Computer Vision Research Groups & Companies
- awesome-deep-learning
- awesome-machine-learning
- Cat Paper Collection
- Computer Vision News
- Most cited researchers on Google scholars
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