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pro vyhledávání: '"Actor, A. A."'
A machine-learnable variational scheme using Gaussian radial basis functions (GRBFs) is presented and used to approximate linear problems on bounded and unbounded domains. In contrast to standard mesh-free methods, which use GRBFs to discretize stron
Externí odkaz:
http://arxiv.org/abs/2410.06219
We present a domain decomposition strategy for developing structure-preserving finite element discretizations from data when exact governing equations are unknown. On subdomains, trainable Whitney form elements are used to identify structure-preservi
Externí odkaz:
http://arxiv.org/abs/2406.05571
Autor:
Dean J. Kareemo, Christina S. Winborn, Samantha S. Olah, Carley N. Miller, JungMin Kim, Chelsie A. Kadgien, Hannah S. Actor-Engel, Harrison J. Ramsay, Austin M. Ramsey, Jason Aoto, Matthew J. Kennedy
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract Tools for visualizing and manipulating protein dynamics in living cells are critical for understanding cellular function. Here we leverage recently available monoclonal antibody sequences to generate a set of affinity tags for labeling and m
Externí odkaz:
https://doaj.org/article/6994164957e4485a81f9f49a4d105b62
Causal representation learning algorithms discover lower-dimensional representations of data that admit a decipherable interpretation of cause and effect; as achieving such interpretable representations is challenging, many causal learning algorithms
Externí odkaz:
http://arxiv.org/abs/2310.18471
Publikováno v:
Frontiers in Mechanical Engineering, Vol 10 (2024)
Representation learning algorithms are often used to extract essential features from high-dimensional datasets. These algorithms commonly assume that such features are independent. However, multimodal datasets containing complementary information oft
Externí odkaz:
https://doaj.org/article/4c3c52496fb6436d8c9d06a7d2d97175
Using neural networks to solve variational problems, and other scientific machine learning tasks, has been limited by a lack of consistency and an inability to exactly integrate expressions involving neural network architectures. We address these lim
Externí odkaz:
http://arxiv.org/abs/2110.14055
Autor:
Celaya, Adrian, Actor, Jonas A., Muthusivarajan, Rajarajeswari, Gates, Evan, Chung, Caroline, Schellingerhout, Dawid, Riviere, Beatrice, Fuentes, David
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling th
Externí odkaz:
http://arxiv.org/abs/2104.10745
Akademický článek
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Publikováno v:
In Handbook of Numerical Analysis 2024 25:469-514
Publikováno v:
In Journal of Computational Physics 1 January 2024 496