Zobrazeno 1 - 10
of 10 484
pro vyhledávání: '"physics-informed neural networks"'
Physics-informed neural networks (PINNs) have great potential for flexibility and effectiveness in forward modeling and inversion of seismic waves. However, coordinate-based neural networks (NNs) commonly suffer from the "spectral bias" pathology, wh
Externí odkaz:
http://arxiv.org/abs/2409.03536
Physics Informed Neural Networks (PINNs) offer several advantages when compared to traditional numerical methods for solving PDEs, such as being a mesh-free approach and being easily extendable to solving inverse problems. One promising approach for
Externí odkaz:
http://arxiv.org/abs/2409.01949
The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highlighted by the Jevons paradox. We propose an attention-enhanced quantum ph
Externí odkaz:
http://arxiv.org/abs/2409.01626
Physics-informed neural networks have gained popularity as a deep-learning based method for solving problems governed by partial differential equations. Especially for engineering applications, this new method seems to be promising since it can solve
Externí odkaz:
http://arxiv.org/abs/2408.17364
Autor:
Cho, Woojin, Jo, Minju, Lim, Haksoo, Lee, Kookjin, Lee, Dongeun, Hong, Sanghyun, Park, Noseong
Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics. In applications such as design optimization or uncertainty quantification, solutions of th
Externí odkaz:
http://arxiv.org/abs/2408.09446
Physics-informed neural networks (PINNs) represent a significant advancement in scientific machine learning by integrating fundamental physical laws into their architecture through loss functions. PINNs have been successfully applied to solve various
Externí odkaz:
http://arxiv.org/abs/2407.10836
Autor:
Trahan, Corey1 (AUTHOR) corey.j.trahan@erdc.dren.mil, Loveland, Mark1 (AUTHOR), Dent, Samuel1 (AUTHOR)
Publikováno v:
Entropy. Aug2024, Vol. 26 Issue 8, p649. 17p.
Autor:
Rahman, Jamshaid Ul, Nimra
Physics-Informed Neural Networks (PINNs) are regarded as state-of-the-art tools for addressing highly nonlinear problems based on partial differential equations. Despite their broad range of applications, PINNs encounter several performance challenge
Externí odkaz:
http://arxiv.org/abs/2409.03239
Physics-informed neural networks (PINNs) are an emerging technique to solve partial differential equations (PDEs). In this work, we propose a simple but effective PINN approach for the phase-field model of ferroelectric microstructure evolution. This
Externí odkaz:
http://arxiv.org/abs/2409.02959
Studying the magnetic field properties on the solar surface is crucial for understanding the solar and heliospheric activities, which in turn shape space weather in the solar system. Surface Flux Transport (SFT) modelling helps us to simulate and ana
Externí odkaz:
http://arxiv.org/abs/2409.01744