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literature.html
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Predicting material properties
https://www.nist.gov/programs-projects/materials-design-toolkit
· https://www.nist.gov/programs-projects/material-qualification
· http://chimad.northwestern.edu/
· https://www.nist.gov/programs-projects/data-and-computational-tools-advanced-materials-design-structural-materials
Learning resources for deep learning
https://machine-learning-for-physicists.org/
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.99.015014
https://science.sciencemag.org/content/349/6245/255
https://doi.org/10.1002/advs.201801367
https://journals.aps.org/prx/abstract/10.1103/PhysRevX.8.031084
RL in Physics
https://arxiv.org/pdf/1902.02157v1.pdf contains a big list of references to RL in physics refs [3-15].
http://inverseprobability.com/talks/notes/deep-gaussian-processes.html
https://www.osti.gov/biblio/1163230
https://blogs.nvidia.com/blog/2015/11/16/vasp/
http://ceur-ws.org/Vol-1990/paper-03.pdf
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.84.201402
https://pubs.acs.org/doi/abs/10.1021/jz400601t
https://www.nature.com/articles/s42256-018-0006-z
https://www.nature.com/articles/s42256-018-0006-z
https://www.nature.com/articles/nnano.2015.309
https://pubs.acs.org/doi/abs/10.1021/jz400601t
https://www.nature.com/articles/s42256-018-0006-z
https://www.nature.com/articles/s41467-018-06322-x
https://arxiv.org/abs/1902.06838
https://www.technologyreview.com/s/612470/uber-has-cracked-two-classic-80s-video-games-by-giving-an-ai-algorithm-a-new-type-of-memory/
https://blogs.nvidia.com/blog/2019/03/18/gaugan-photorealistic-landscapes-nvidia-research/?ncid=so-you-n1-78256
https://pubs.rsc.org/en/content/articlehtml/2019/tc/c8tc05554h
https://www.rsc.org/journals-books-databases/open-access/gold-open-access/
https://arxiv.org/pdf/1902.07685.pdf
https://machinelearningmastery.com/recommendations-for-deep-learning-neural-network-practitioners/
https://arxiv.org/pdf/1410.3831.pdf
https://narang.seas.harvard.edu/
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.064104
https://github.com/NVlabs/stylegan
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.023304
https://www.sciencedirect.com/science/article/pii/S0927025618301526?via%3Dihub
https://arxiv.org/abs/1703.06114
https://pubs.acs.org/doi/10.1021/acs.jctc.8b00908
https://pubs.acs.org/doi/abs/10.1021/jacs.8b13420
https://papers.nips.cc/paper/5851-deep-convolutional-inverse-graphics-network
https://link.springer.com/chapter/10.1007/978-3-319-46604-0_20
https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/
https://arxiv.org/abs/1710.11431
https://wiki.tum.de/display/lfdv/Deep+Residual+Networks
https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201801367
https://applied-data.science/static/main/res/alpha_go_zero_cheat_sheet.png
https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/
https://arxiv.org/pdf/1902.02157v1.pdf
https://journals.aps.org/pra/pdf/10.1103/PhysRevA.39.3761
https://medium.com/nanonets/how-to-easily-detect-objects-with-deep-learning-on-raspberrypi-225f29635c74
https://github.com/Avsecz/kopt
http://ceur-ws.org/Vol-1990/paper-03.pdf
https://blogs.nvidia.com/blog/2015/11/16/vasp/
https://www.osti.gov/biblio/1163230
https://www.cambridge.org/core/journals/mrs-bulletin/issue/computational-design-and-development-of-alloys/19F2FFB51CE942352452E6291E73902A
http://inverseprobability.com/talks/notes/deep-gaussian-processes.html
https://www.cambridge.org/core/journals/mrs-bulletin/issue/computational-design-and-development-of-alloys/19F2FFB51CE942352452E6291E73902
Here is some info from my side:
ML for (partial) differential equation and similar:
- This is the link to one prof’s webpage who does research in that direction. https://www.brown.edu/research/projects/crunch/current-research-v2/physics-informed-deep-learning
These are links to some of the actual papers:
https://www.brown.edu/research/projects/crunch/sites/brown.edu.research.projects.crunch/files/uploads/J.%20Comp.%20Phys.pdf
https://arxiv.org/abs/1711.10561
https://arxiv.org/abs/1711.10566
Photonics related:
- A more general overview of ML in photonics, slightly over 1 year old: https://www.nature.com/articles/s41566-017-0058-3
Related to photonics but not only:
- Deep learning for determining a near-optimal topological design without any iteration - https://arxiv.org/ftp/arxiv/papers/1801/1801.05463.pdf
This is an example of ML for optimization
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