Zobrazeno 1 - 10
of 10 857
pro vyhledávání: '"A. Deodhar"'
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations, enhancing the generalization of traditional neural networks. However
Externí odkaz:
http://arxiv.org/abs/2406.14715
Autor:
A. Deodhar, V. Strand, P. G. Conaghan, E. Sullivan, S. Blackburn, H. Tian, K. Gandhi, S. M. Jugl, R. Alten
Publikováno v:
BMC Rheumatology, Vol 4, Iss 1, Pp 1-11 (2020)
Abstract Background Symptoms and comorbidities of ankylosing spondylitis (AS) considerably reduce health-related quality of life (HRQoL) and ability to work. This real-world study assessed rates of tumour necrosis factor inhibitor (TNFi) use and swit
Externí odkaz:
https://doaj.org/article/3bce0e9a1ae94ba2a76bd709465498b3
Autor:
A. Deodhar, P. J. Mease, I. B. McInnes, X. Baraliakos, K. Reich, A. Blauvelt, C. Leonardi, B. Porter, A. Das Gupta, A. Widmer, L. Pricop, T. Fox
Publikováno v:
Arthritis Research & Therapy, Vol 21, Iss 1, Pp 1-11 (2019)
Abstract Background Secukinumab, a fully human immunoglobulin G1-kappa monoclonal antibody that directly inhibits interleukin (IL)-17A, has been shown to have robust efficacy in the treatment of moderate-to-severe psoriasis (PsO), psoriatic arthritis
Externí odkaz:
https://doaj.org/article/993397c83abf4290baa620330d69955a
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have to be retrained with every change in initial-boundary conditions and PD
Externí odkaz:
http://arxiv.org/abs/2308.09290
Autor:
Atwood, James, Tian, Tina, Packer, Ben, Deodhar, Meghana, Chen, Jilin, Beutel, Alex, Prost, Flavien, Beirami, Ahmad
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present. In this p
Externí odkaz:
http://arxiv.org/abs/2307.05728
Autor:
Filip Van den Bosch, Atul Deodhar, Denis Poddubnyy, Walter P. Maksymowych, Désirée van derHeijde, Tae‐Hwan Kim, Mitsumasa Kishimoto, Xenofon Baraliakos, Yihan Li, Kristin D'Silva, Peter Wung, In‐Ho Song
Publikováno v:
ACR Open Rheumatology, Vol 6, Iss 8, Pp 470-480 (2024)
Objective Upadacitinib improved the signs and symptoms of non‐radiographic axial spondyloarthritis (nr‐axSpA) versus placebo over 14 weeks in the primary analysis of the SELECT‐AXIS 2 nr‐axSpA study. Here, we evaluated the efficacy and safety
Externí odkaz:
https://doaj.org/article/db103b5345e54f818689f724ae2c5979
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitations of PINNs is that the neural solutions are challenging to interpret,
Externí odkaz:
http://arxiv.org/abs/2303.07009
Autor:
Gupta, Samrat, Deodhar, Swanand
Publikováno v:
Information Technology & People, 2021, Vol. 37, Issue 2, pp. 919-943.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ITP-10-2020-0682
Autor:
Saraf, Mandar, Chinnappan, Pandi Raj, Deodhar, Aditya, Jangra, Mamta, Krishnamoorthi, J., Majumder, Gobinda, Padmavathy, Veera, Ravindran, K. C., Shah, Raj Bhupen, Shinde, Ravindra, Satyanarayana, B.
Publikováno v:
JINST, 18, (2023), P05003
The INO collaboration is designing a cosmic muon veto detector (CMVD) to cover the mini-ICAL detector which is operational at the IICHEP transit campus, Madurai in South India. The aim of the CMVD is to study the feasibility of building an experiment
Externí odkaz:
http://arxiv.org/abs/2301.12486
Autor:
Majumdar, Ritam, Jadhav, Vishal, Deodhar, Anirudh, Karande, Shirish, Vig, Lovekesh, Runkana, Venkataramana
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enabl
Externí odkaz:
http://arxiv.org/abs/2212.10032