Zobrazeno 1 - 10
of 461
pro vyhledávání: '"scientific machine learning"'
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:
Waleed Diab, Mohammed Al Kobaisi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Learning operators with deep neural networks is an emerging paradigm for scientific computing. Deep Operator Network (DeepONet) is a modular operator learning framework that allows for flexibility in choosing the kind of neural network to be
Externí odkaz:
https://doaj.org/article/5b4ebe4bf3be492aadf2bbd3b525d581
Publikováno v:
Aerospace Research Communications, Vol 2 (2024)
This work introduces Pontryagin Neural Networks (PoNNs), a specialised subset of Physics-Informed Neural Networks (PINNs) that aim to learn optimal control actions for optimal control problems (OCPs) characterised by integral quadratic cost functions
Externí odkaz:
https://doaj.org/article/a21353faef194760a8ada96a43d89b43
Publikováno v:
Journal of Integrative Bioinformatics, Vol 21, Iss 1, Pp 524-31 (2024)
Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for
Externí odkaz:
https://doaj.org/article/6ae0cf3ace624c3f97687bbf28d30f80
Autor:
Ben Noordijk, Monica L. Garcia Gomez, Kirsten H. W. J. ten Tusscher, Dick de Ridder, Aalt D. J. van Dijk, Robert W. Smith
Publikováno v:
Frontiers in Systems Biology, Vol 4 (2024)
Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic mode
Externí odkaz:
https://doaj.org/article/330e48d93cd647ec83f274ca3c660175
Publikováno v:
IEEE Access, Vol 12, Pp 153253-153272 (2024)
Scientific machine learning and physics-informed neural networks are novel conceptual approaches that integrate scientific knowledge with methods from data science and deep learning. This emerging field has garnered increasing interest due to its uni
Externí odkaz:
https://doaj.org/article/acba6d585ea54063b0297c2db3bbeb15
Publikováno v:
IEEE Access, Vol 12, Pp 147753-147761 (2024)
Differential equations play a significant role in modeling of real world dynamical problems. A large amount of prior physical information in the form of differential equations are inherited in the dynamical systems. However, the black box machine lea
Externí odkaz:
https://doaj.org/article/6e585cd7e558486b853a379431ebe329
Publikováno v:
IEEE Access, Vol 12, Pp 93823-93840 (2024)
Adverse Drug Reactions(ADRs) due to drug-drug interactions present a public health problem worldwide that deserves attention due to its impact on mortality, morbidity, and healthcare costs. There have been major challenges in healthcare with the ever
Externí odkaz:
https://doaj.org/article/d46eac45d1be4607889c32a730a37b17
Autor:
Ugurcan Celik, Mustafa Umut Demirezen
Publikováno v:
IEEE Access, Vol 12, Pp 16805-16829 (2024)
This study presents an innovative approach that utilizes scientific machine learning and two types of enhanced neural networks for modeling a parametric guidance algorithm within the framework of ordinary differential equations to optimize the landin
Externí odkaz:
https://doaj.org/article/6def7a7f35ee4cb7b55128909078deb1
Autor:
Waqar Muhammad Ashraf, Vivek Dua
Publikováno v:
Energy and AI, Vol 16, Iss , Pp 100363- (2024)
Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrat
Externí odkaz:
https://doaj.org/article/8089d5d76cc74ca4b84bd13e337387ab