Zobrazeno 1 - 10
of 1 061
pro vyhledávání: '"DeepONet"'
Autor:
Diab, Waleed1, Al Kobaisi, Mohammed1 mohammed.alkobaisi@ku.ac.ae
Publikováno v:
Scientific Reports. 9/12/2024, Vol. 14 Issue 1, p1-12. 12p.
The Maxwell's equations, a system of linear partial differential equations (PDEs), describe the behavior of electric and magnetic fields in time and space and is essential for many important electromagnetic applications. Although numerical methods ha
Externí odkaz:
http://arxiv.org/abs/2411.18733
Autor:
Yang, Yahong
In this paper, we investigate the application of operator learning, specifically DeepONet, to solve partial differential equations (PDEs). Unlike function learning methods that require training separate neural networks for each PDE, operator learning
Externí odkaz:
http://arxiv.org/abs/2410.04344
Full waveform inversion (FWI) plays a crucial role in the field of geophysics. There has been lots of research about applying deep learning (DL) methods to FWI. The success of DL-FWI relies significantly on the quantity and diversity of the datasets.
Externí odkaz:
http://arxiv.org/abs/2408.08005
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed losses for downstream tasks. O
Externí odkaz:
http://arxiv.org/abs/2411.07239
Geological carbon sequestration (GCS) involves injecting CO$_2$ into subsurface geological formations for permanent storage. Numerical simulations could guide decisions in GCS projects by predicting CO$_2$ migration pathways and the pressure distribu
Externí odkaz:
http://arxiv.org/abs/2409.16572
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep opera
Externí odkaz:
http://arxiv.org/abs/2409.15683
Autor:
Ahmed, Bilal, Qiu, Yuqing, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Mobasher, Mostafa E.
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, and considerable time. This study addresses this challenge by introducin
Externí odkaz:
http://arxiv.org/abs/2409.00994
DeepONet has recently been proposed as a representative framework for learning nonlinear mappings between function spaces. However, when it comes to approximating solution operators of partial differential equations (PDEs) with discontinuous solution
Externí odkaz:
http://arxiv.org/abs/2408.04157
Autor:
Kumar, Varun, Goswami, Somdatta, Kontolati, Katiana, Shields, Michael D., Karniadakis, George Em
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine l
Externí odkaz:
http://arxiv.org/abs/2408.02198