Zobrazeno 1 - 10
of 5 138
pro vyhledávání: '"Physics-informed"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 2963-2985.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-11-2023-0709
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 3131-3165.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-09-2023-0568
Publikováno v:
Chinese Journal of Mechanical Engineering, Vol 37, Iss 1, Pp 1-17 (2024)
Abstract As a large-scale mining excavator, the electric shovel (ES) has been extensively employed in open-pit mines for overburden removal and mineral loading. In the development of unmanned operations for ES, dynamic excavation trajectory planning
Externí odkaz:
https://doaj.org/article/11e5588668b0474e9194654e46c30391
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Abstract To achieve the desired superheat of molten steel during the continuous casting process, optimization of process parameters such as molten steel temperature in ladle furnace, casting speed, and baking temperature is necessary. Therefore, obta
Externí odkaz:
https://doaj.org/article/c99fe18c972441aaad37aa17967ebe74
Autor:
Hanwen Bi, Thushara D. Abhayapala
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-14 (2024)
Abstract Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome the
Externí odkaz:
https://doaj.org/article/a80b8f4e9c81417d8b5370fa8592f3ba
Publikováno v:
Geothermal Energy, Vol 12, Iss 1, Pp 1-25 (2024)
Abstract Deep learning has gained attention as a potentially powerful technique for modeling natural-state geothermal systems; however, its physical validity and prediction inaccuracy at extrapolation ranges are limiting. This study proposes the use
Externí odkaz:
https://doaj.org/article/98dec23e81004fc1a283719de796a275
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract This research introduces an accelerated training approach for Vanilla Physics-Informed Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial weight state of the neural network, the ratio of domain to b
Externí odkaz:
https://doaj.org/article/1508b60fcdee41968b3e3f5e5da2b254
Autor:
Jihahm Yoo, Haesung Lee
Publikováno v:
AIMS Mathematics, Vol 9, Iss 10, Pp 27000-27027 (2024)
In this paper, we study physics-informed neural networks (PINN) to approximate solutions to one-dimensional boundary value problems for linear elliptic equations and establish robust error estimates of PINN regardless of the quantities of the coeffic
Externí odkaz:
https://doaj.org/article/97ba240a77c5468c9958ebc08a727f12
Publikováno v:
Metrology, Vol 4, Iss 3, Pp 489-505 (2024)
As modern systems become more complex, their control strategy no longer relies only on measurement data from probes; it also requires information from mathematical models for non-measurable places. On the other hand, those mathematical models can lea
Externí odkaz:
https://doaj.org/article/a2e62ec8a78342b7b2e3d0e8561e7325
Publikováno v:
Autonomous Intelligent Systems, Vol 4, Iss 1, Pp 1-13 (2024)
Abstract Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a phy
Externí odkaz:
https://doaj.org/article/a2edac8743e24e87b98554729e2df3e5