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pro vyhledávání: '"Archie, J. P."'
This research contributes to the advancement of traffic state estimation methods by leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning framework. The classical LWR model, while useful, falls short of accurately
Externí odkaz:
http://arxiv.org/abs/2308.11818
Autor:
Huang, Archie J., Agarwal, Shaurya
Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the evolution of systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence p
Externí odkaz:
http://arxiv.org/abs/2302.12337
Autor:
Huang, Archie J., Agarwal, Shaurya
A recent development in machine learning - physics-informed deep learning (PIDL) - presents unique advantages in transportation applications such as traffic state estimation. Consolidating the benefits of deep learning (DL) and the governing physical
Externí odkaz:
http://arxiv.org/abs/2302.12336
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 900-908 (2023)
Nonlocal calculus-based macroscopic traffic models overcome the limitations of classical local models in accurately capturing traffic flow dynamics. These models incorporate “nonlocal” elements by considering the speed as a weighted mean of downs
Externí odkaz:
https://doaj.org/article/ee5ff3af52b44b7c8371403fb98a0cd1
Autor:
Archie J. Huang, Shaurya Agarwal
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 279-293 (2023)
Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence points to the
Externí odkaz:
https://doaj.org/article/83f8f571d2e240e0b827af7415738eda
Akademický článek
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Autor:
Archie J. Huang, Shaurya Agarwal
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 3, Pp 503-518 (2022)
We present a physics-informed deep learning (PIDL) approach to tackle the challenge of data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep learning (DL) neural network with the knowledge of traffic flow theory to
Externí odkaz:
https://doaj.org/article/73ec527f6dbf423899fbfcfb483c8ebe
Autor:
Vivien H. C. Bramwell, Marion V. Burgers, Robert L. Souhami, Antonie H. M. Taminiau, Jan W. Van Der Eijken, Alan W. Craft, Archie J. Malcolm, Barbara Uscinska, Anna L. Kirkpatrick, David Machin, Martine M. Van Glabbeke
Publikováno v:
Sarcoma, Vol 1, Iss 3-4, Pp 155-160 (1997)
Purpose. To report the outcome of 37 patients with metastatic osteosarcoma entered into a large randomized trial (EOI 80831/MRC B002) comparing two different regimens of chemotherapy in patients with osteosarcoma.
Externí odkaz:
https://doaj.org/article/0ea601920e044d65a418769442b52a1d
Publikováno v:
IEEE Systems Journal; December 2020, Vol. 14 Issue: 4 p5187-5198, 12p
Akademický článek
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