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Education Full Text (H.W. Wilson)

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Reinforcement learning-guided control strategies for CAR T-cell activation and expansion.

• Authors : Ferdous S; Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.; Shihab IF

Subjects: Machine Learning* ; T-Lymphocytes*/T-Lymphocytes*/T-Lymphocytes*/immunology; Humans

• Source: Biotechnology and bioengineering [Biotechnol Bioeng] 2024 Sep; Vol. 121 (9), pp. 2868-2880. Date of Electronic Publication: 2024 May 29.Publisher: Wiley Country of Publication: United States NLM ID: 7502021 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0290

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Applied Science & Technology Full Text (H.W. Wilson)

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A physics-based predictive model for pulse design to realize high-performance memristive neural networks.

• Authors : Deng, Haoyue¹; Fan, Zhen¹ ; Dong, Shuai¹

Subjects: *COMPUTATIONAL physics; *PREDICTION models; *MACHINE learning

• Source: APL Machine Learning. Dec2023, Vol. 1 Issue 4, p1-12. 12p.

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Iterative sampling of expensive simulations for faster deep surrogate training*.

• Authors : Gaffney, Jim A.¹ (AUTHOR) ; Humbird, Kelli¹ (AUTHOR); Kruse, Michael¹ (AUTHOR)

Subjects: *TASK analysis; *COMPUTATIONAL physics

• Source: Contributions to Plasma Physics. Jun2023, Vol. 63 Issue 5/6, p1-10. 10p.

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RoeNet: Predicting discontinuity of hyperbolic systems from continuous data.

• Authors : Tong, Yunjin; Xiong, Shiying; He, Xingzhe

Subjects: ARTIFICIAL neural networks; HYPERBOLIC differential equations; SCIENCE education

• Source: International Journal for Numerical Methods in Engineering; 3/30/2024, Vol. 125 Issue 6, p1-21, 21p

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Multiphysics machine learning framework for on-demand multi-functional nano pattern design by light-controlled capillary force lithography.

• Authors : Chapagain, Ashish; Cho, In Ho

Subjects: MACHINE learning; LITHOGRAPHY; ARTIFICIAL intelligence

• Source: Communications Physics; 7/2/2024, Vol. 7 Issue 1, p1-12, 12p

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Constructing coarse-grained models with physics-guided Gaussian process regression.

• Authors : Fang, Yating; Zhao, Qian Qian; Sills, Ryan B.

Subjects: GAUSSIAN processes; VELOCITY; ATMOSPHERIC models

• Source: APL Machine Learning; Jun2024, Vol. 2 Issue 2, p1-17, 17p

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The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning.

• Authors : Hinz, J.; Yu, Dayou; Pandey, Deep Shankar

Subjects: THERMODYNAMICS; EQUATIONS of state; MACHINE learning

• Source: APL Machine Learning; Jun2024, Vol. 2 Issue 2, p1-12, 12p

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Fluid flow diffusion modelling with convolutional neural network aided method.

• Authors : Mateev, Valentin; Marinova, Iliana

Subjects: CONVOLUTIONAL neural networks; FLUID flow; COMPUTATIONAL physics

• Source: AIP Conference Proceedings; 2023, Vol. 2939 Issue 1, p1-6, 6p

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