Zobrazeno 1 - 10
of 6 410
pro vyhledávání: '"surrogate modelling"'
Reduced-order models, also known as proxy model or surrogate model, are approximate models that are less computational expensive as opposed to fully descriptive models. With the integration of machine learning, these models have garnered increasing r
Externí odkaz:
http://arxiv.org/abs/2409.09920
Autor:
Beaver, Nicholas, Dive, Aniruddha, Wong, Marina, Shimanuki, Keita, Patil, Ananya, Ferrell, Anthony, Kivy, Mohsen B.
In an effort to develop a rapid, reliable, and cost-effective method for predicting the structure of single-phase high entropy alloys, a Graph Neural Network (ALIGNN-FF) based approach was introduced. This method was successfully tested on 132 differ
Externí odkaz:
http://arxiv.org/abs/2409.07664
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architectu
Externí odkaz:
http://arxiv.org/abs/2407.20091
Publikováno v:
Water Research, 2024, 263, 122142
Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potent
Externí odkaz:
http://arxiv.org/abs/2404.10324
Several related works have introduced Koopman-based Machine Learning architectures as a surrogate model for dynamical systems. These architectures aim to learn non-linear measurements (also known as observables) of the system's state that evolve by a
Externí odkaz:
http://arxiv.org/abs/2405.06425
Publikováno v:
Comput. Methods Appl. Mech. Engrg. 432 (2024) 117423
Surrogate models provide a quick-to-evaluate approximation to complex computational models and are essential for multi-query problems like design optimisation. The inputs of current deterministic computational models are usually high-dimensional and
Externí odkaz:
http://arxiv.org/abs/2404.14857
Publikováno v:
International Journal for Uncertainty Quantification, 2024, 14 (1), pp.43-60
This paper deals with surrogate modelling of a computer code output in a hierarchical multi-fidelity context, i.e., when the output can be evaluated at different levels of accuracy and computational cost. Using observations of the output at low- and
Externí odkaz:
http://arxiv.org/abs/2312.02575
Autor:
Hadi, Ahmed1 (AUTHOR) A.H.Hadi-1@tudelft.nl, Moradi, Morteza2 (AUTHOR), Pang, Yusong1 (AUTHOR), Schott, Dingena1 (AUTHOR)
Publikováno v:
Scientific Reports. 11/6/2024, Vol. 14 Issue 1, p1-20. 20p.
Operator-based neural network architectures such as DeepONets have emerged as a promising tool for the surrogate modeling of physical systems. In general, towards operator surrogate modeling, the training data is generated by solving the PDEs using t
Externí odkaz:
http://arxiv.org/abs/2402.16903
Autor:
Borisova, Julia, Nikitin, Nikolay O.
The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automa
Externí odkaz:
http://arxiv.org/abs/2312.04330