المؤلفون: Jihahm Yoo, Haesung Lee

المصدر: AIMS Mathematics, Vol 9, Iss 10, Pp 27000-27027 (2024)

مصطلحات موضوعية: sobolev spaces, boundary value problems, existence and uniqueness, physics-informed neural networks (pinn), $ l^2 $-contraction estimates, error estimates, Mathematics, QA1-939

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2473-6988

URL الوصول: https://doaj.org/article/97ba240a77c5468c9958ebc08a727f12

المؤلفون: Seohee Jang, Mohammad Jadidi, Saleh Rezaeiravesh, Alistair Revell, Yasser Mahmoudi

المصدر: Machine Learning: Science and Technology, Vol 5, Iss 3, p 035030 (2024)

مصطلحات موضوعية: porous flow, composite porous-fluid system, physics-informed neural networks (PINN), turbulent flow, Reynolds-averaged Navier-Stokes (RANS), Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Relation: https://doi.org/10.1088/2632-2153/ad63f4; https://doaj.org/toc/2632-2153; https://doaj.org/article/9097d28c9fa642ec96197d447f38c8a4

الاتاحة: https://doi.org/10.1088/2632-2153/ad63f4
https://doaj.org/article/9097d28c9fa642ec96197d447f38c8a4

المؤلفون: Chengping Rao, Hao Sun, Yang Liu

المصدر: Theoretical and Applied Mechanics Letters, Vol 10, Iss 3, Pp 207-212 (2020)

مصطلحات موضوعية: Physics-informed neural networks (PINN), Deep learning, Fluid dynamics, Incompressible laminar flow, Engineering (General). Civil engineering (General), TA1-2040

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2095034920300350; https://doaj.org/toc/2095-0349

URL الوصول: https://doaj.org/article/02626ea4f6b341289d0ac0411c4b628e

المؤلفون: Yuting Yang, Gang Mei

المصدر: Mathematics; Volume 10; Issue 16; Pages: 2945

مصطلحات موضوعية: soil–water infiltration, numerical investigation, deep learning, physics-informed neural networks (PINN)

وصف الملف: application/pdf

Relation: Mathematics and Computer Science; https://dx.doi.org/10.3390/math10162945

الاتاحة: https://doi.org/10.3390/math10162945

المؤلفون: Yi Huang, Zhiyu Zhang, Xing Zhang

المصدر: Fluids; Volume 7; Issue 2; Pages: 56

مصطلحات موضوعية: physics-informed neural networks (PINN), direct-forcing immersed boundary method, incompressible laminar flow, circular cylinder

وصف الملف: application/pdf

Relation: Mathematical and Computational Fluid Mechanics; https://dx.doi.org/10.3390/fluids7020056

الاتاحة: https://doi.org/10.3390/fluids7020056

المؤلفون: Izzatullah, Muhammad, Yildirim, Isa Eren, Bin Waheed, Umair, Alkhalifah, Tariq Ali

المساهمون: Earth Science and Engineering Program, King Abdulllah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, Physical Science and Engineering (PSE) Division, Seismic Wave Analysis Group, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia

مصطلحات موضوعية: physics-informed neural networks (PINN), uncertainty quantification, Laplace approximation, hypocenter localization, microseismic, inversion, deep learning

وصف الملف: application/pdf

Relation: github:izzatum/laplace-hypopinn; https://iopscience.iop.org/article/10.1088/2632-2153/ac94b3; 2205.14439; Izzatullah, M., Yildirim, I. E., Waheed, U. B., & Alkhalifah, T. (2022). Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty. Machine Learning: Science and Technology, 3(4), 045001. https://doi.org/10.1088/2632-2153/ac94b3; MACHINE LEARNING-SCIENCE AND TECHNOLOGY; 045001; http://hdl.handle.net/10754/683709; WOS:000864902700001

الاتاحة: http://hdl.handle.net/10754/683709
https://doi.org/10.1088/2632-2153/ac94b3