Zobrazeno 1 - 10
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pro vyhledávání: '"Deodhar, A"'
Deep operator networks (DeepONet) and neural operators have gained significant attention for their ability to map infinite-dimensional function spaces and perform zero-shot super-resolution. However, these models often require large datasets for effe
Externí odkaz:
http://arxiv.org/abs/2409.09207
Autor:
Liu, Quanliang, Polak, Maciej P., Kim, So Yeon, Shuvo, MD Al Amin, Deodhar, Hrishikesh Shridhar, Han, Jeongsoo, Morgan, Dane, Oh, Hyunseok
Materials design often relies on human-generated hypotheses, a process inherently limited by cognitive constraints such as knowledge gaps and limited ability to integrate and extract knowledge implications, particularly when multidisciplinary experti
Externí odkaz:
http://arxiv.org/abs/2409.06756
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:
Xenofon Baraliakos, Désirée van der Heijde, Joachim Sieper, Robert Davies Inman, Hideto Kameda, Walter Peter Maksymowych, Ivan Lagunes-Galindo, Xianwei Bu, Peter Wung, Koji Kato, Anna Shmagel, Atul Deodhar
Publikováno v:
Arthritis Research & Therapy, Vol 26, Iss 1, Pp 1-13 (2024)
Abstract Background The efficacy and safety of upadacitinib in patients with ankylosing spondylitis (AS) and inadequate response/intolerance to biologic disease-modifying antirheumatic drugs (bDMARD-IR) were evaluated through 1 year in the SELECT-AXI
Externí odkaz:
https://doaj.org/article/1259403985ce47a0a402e64210800be8
Publikováno v:
BMC Sports Science, Medicine and Rehabilitation, Vol 16, Iss 1, Pp 1-10 (2024)
Abstract Background Axial spondyloarthritis (axSpA) is a chronic inflammatory disease which mainly affects the spine and sacroiliac joints, causing longstanding back pain, stiffness, and limited mobility. AxSpA is an underrecognized disease in non-rh
Externí odkaz:
https://doaj.org/article/afa8e39a08114c7d97be3e1525fd6216
Autor:
Jeffrey R. Curtis, Atul Deodhar, Enrique R. Soriano, Emmanouil Rampakakis, May Shawi, Natalie J. Shiff, Chenglong Han, William Tillett, Dafna D. Gladman
Publikováno v:
Rheumatology and Therapy, Vol 11, Iss 6, Pp 1501-1517 (2024)
Abstract Introduction Patterns of treatment response can inform clinical decision-making. This study assessed the course and impact of achieving minimal clinically important improvement (MCII) in clinical measures and patient-reported outcomes (PROs)
Externí odkaz:
https://doaj.org/article/631e6dc4cc6a4bb893e0571db9fd29f2
Autor:
Philip Mease, Tatiana Korotaeva, Pavel Shesternya, Muza Kokhan, Anton Rukavitsyn, Dmitry Vasilchenkov, Mohamed Sharaf, Frédéric Lavie, Atul Deodhar
Publikováno v:
Rheumatology and Therapy, Vol 11, Iss 6, Pp 1551-1567 (2024)
Abstract Introduction There are limited data on the use of advanced therapies to treat psoriatic arthritis (PsA) in Russia. Guselkumab, an interleukin (IL)-23p19-subunit inhibitor, demonstrated efficacy in patients with PsA in the phase 3 DISCOVER-1
Externí odkaz:
https://doaj.org/article/45579ebd45974493ad26516b7a01fa05
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:
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