Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Mohannad Elhamod"'
Publikováno v:
Advanced Photonics Research, Vol 3, Iss 11, Pp n/a-n/a (2022)
Herein, example of study of optical modes propagating through spatially periodic composites is used to demonstrate that embedding physics‐driven constraints into machine‐learning process can dramatically improve accuracy and generalizability of r
Externí odkaz:
https://doaj.org/article/51dbcda9800247db8e1468ec3f52d336
Autor:
Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 13:1-23
Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network outputs with known physics), along with the supervision contained in data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a34eded086f2e499939c9fa643e13ed
https://doi.org/10.1145/3530911
https://doi.org/10.1145/3530911
Autor:
Yasin Bakis, Mohannad Elhamod, Jeremy Leipzig, Paula M. Mabee, Kelly Diamond, Anuj Karpatne, Wasila M. Dahdul, Jane Greenberg, Brian B. Avants, Henry L. Bart, A. Murat Maga
Publikováno v:
Methods in Ecology and Evolution. 13:642-652
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fd613e6b0de6bf14199bd4c5a380811
https://doi.org/10.1111/2041-210x.13768
https://doi.org/10.1111/2041-210x.13768
Publikováno v:
Metamaterials, Metadevices, and Metasystems 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55d43e05f5af979c568c30c979f05a3a
https://doi.org/10.1117/12.2632454
https://doi.org/10.1117/12.2632454
Publikováno v:
Conference on Lasers and Electro-Optics.
We present machine learning techniques that incorporate physics into the training process. We demonstrate, on example of predicting light propagation in multilayered composites, that physics-informed models are significantly more robust than their bl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03021a9876e2c0501f7fe447bac25639
https://doi.org/10.1364/cleo_qels.2022.ftu1b.1
https://doi.org/10.1364/cleo_qels.2022.ftu1b.1
Autor:
Jane Greenberg, Paula M. Mabee, Kelly Diamond, Anuj Karpatne, Yasin Bakis, Wasila M. Dahdul, Mohannad Elhamod, Jeremy Leipzig, A. Murat Maga, Brian B. Avants, Henry L. Bart
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16ae0ea1ee5f875ad5b0b6bcf856a927
https://doi.org/10.1111/2041-210x.13768/v3/response1
https://doi.org/10.1111/2041-210x.13768/v3/response1
Publikováno v:
Metamaterials, Metadevices, and Metasystems 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d4fe6f98c0b19300335acbb6908b41f
https://doi.org/10.1117/12.2594575
https://doi.org/10.1117/12.2594575
Autor:
Jeremy Leipzig, Mohannad Elhamod, Kelly Diamond, Henry L. Bart, Jane Greenberg, Wasila M. Dahdul, Anuj Karpatne, Xiaojun Wang, Yasin Bakis, Paula M. Mabee, A. Murat Maga
Biodiversity image repositories are crucial sources of training data for machine learning approaches to biological research. Metadata, specifically metadata about object quality, is putatively an important prerequisite to selecting sample subsets for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::010a4be40a76659a3192ea4685ef76c1
https://doi.org/10.1101/2021.01.28.428644
https://doi.org/10.1101/2021.01.28.428644
Autor:
Wasila M. Dahdul, Yasin Bakis, Jane Greenberg, Jeremy Leipzig, Kelly Diamond, Paula M. Mabee, Brian B. Avants, Anuj Karpatne, Henry L. Bart, Mohannad Elhamod, A. M. Maga
Species classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution.While conventional approaches for this task us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90ec3a101e6d6e72746889bce2c1bf5c
https://doi.org/10.1101/2021.01.17.427006
https://doi.org/10.1101/2021.01.17.427006
Autor:
Mohannad Elhamod, Anuj Karpatne, Paula M. Mabee, A. Murat Maga, Jane Greenberg, Henry L. Bart, Jeremy Leipzig, Xiaojun Wang, Kelly Diamond, Wasila M. Dahdul, Yasin Bakis
Publikováno v:
Metadata and Semantic Research ISBN: 9783030719029
MTSR
MTSR
Biodiversity image repositories are crucial sources for training machine learning approaches to support biological research. Metadata about object (e.g. image) quality is a putatively important prerequisite to selecting samples for these experiments.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f588c76cd44ea02640eb4ac81308c492
https://doi.org/10.1007/978-3-030-71903-6_1
https://doi.org/10.1007/978-3-030-71903-6_1