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pro vyhledávání: '"Chen, Paula"'
Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. However, two major challenges remain: limited interpretability
Externí odkaz:
http://arxiv.org/abs/2404.08809
We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization pr
Externí odkaz:
http://arxiv.org/abs/2311.07790
Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections with a wide range of fields, including optimal control, differential games, and imaging sciences. By considering the time variable to be a higher dimensional quantity, HJ
Externí odkaz:
http://arxiv.org/abs/2303.12928
Two key challenges in optimal control include efficiently solving high-dimensional problems and handling optimal control problems with state-dependent running costs. In this paper, we consider a class of optimal control problems whose running costs c
Externí odkaz:
http://arxiv.org/abs/2110.02541
Two of the main challenges in optimal control are solving problems with state-dependent running costs and developing efficient numerical solvers that are computationally tractable in high dimension. In this paper, we provide analytical solutions to c
Externí odkaz:
http://arxiv.org/abs/2109.14849
Publikováno v:
In Computers and Mathematics with Applications 1 May 2024 161:90-120
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Akademický článek
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Autor:
Chen, Paula Renee
Increasing muscle mass is one of the primary goals of the United States poultryindustry; however, skeletal muscle development at the molecular level is not fullyunderstood. Myostatin (MSTN) is a well-known negative regulator of muscle growth byinhibi
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1462207424
Autor:
Noland, Rylie S., Redel, Bethany K., LaMartina, Marissa G., Chen, Paula R., Prather, Randall S.
Publikováno v:
Molecular Reproduction & Development; Dec2024, Vol. 91 Issue 12, p1-6, 6p