Zobrazeno 1 - 10
of 432
pro vyhledávání: '"Wang, SiFan"'
Federated Learning (FL) has emerged as an excellent solution for performing deep learning on different data owners without exchanging raw data. However, statistical heterogeneity in FL presents a key challenge, leading to a phenomenon of skewness in
Externí odkaz:
http://arxiv.org/abs/2408.11278
Autor:
Jin, Hai, Mao, Junjie, Chen, Liubiao, Chen, Naihui, Cui, Wei, Gao, Bo, Li, Jinjin, Li, Xinfeng, Liu, Jiejia, Quan, Jia, Jiang, Chunyang, Wang, Guole, Wang, Le, Wang, Qian, Wang, Sifan, Xiao, Aimin, Zhang, Shuo
DIffuse X-ray Explorer (DIXE) is a proposed high-resolution X-ray spectroscopic sky surveyor on the China Space Station (CSS). DIXE will focus on studying hot baryons in the Milky Way. Galactic hot baryons like the X-ray emitting Milky Way halo and e
Externí odkaz:
http://arxiv.org/abs/2406.09813
Autor:
Wang, Sifan, Seidman, Jacob H, Sankaran, Shyam, Wang, Hanwen, Pappas, George J., Perdikaris, Paris
Operator learning is an emerging area of machine learning which aims to learn mappings between infinite dimensional function spaces. Here we uncover a connection between operator learning architectures and conditioned neural fields from computer visi
Externí odkaz:
http://arxiv.org/abs/2405.13998
Diffuse X-ray Explorer (DIXE) is a proposed X-ray spectroscopic survey experiment for the China Space Station. Its detector assembly (DA) contains the transition edge sensor (TES) microcalorimeter and readout electronics based on the superconducting
Externí odkaz:
http://arxiv.org/abs/2405.07559
While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (PDEs), their performance is known to degrade when larger and deeper neu
Externí odkaz:
http://arxiv.org/abs/2402.00326
Recently deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a nov
Externí odkaz:
http://arxiv.org/abs/2308.12939
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be hampered b
Externí odkaz:
http://arxiv.org/abs/2308.08468
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural
Externí odkaz:
http://arxiv.org/abs/2305.11111
Publikováno v:
ICLR 2024
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbation problems. In this work, we present a new training paradigm referred
Externí odkaz:
http://arxiv.org/abs/2302.13143
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 6, Pp 51-58 (2024)
With the development of miniaturized wearable electronics,flexible energy storage devices with soft,flexible,small size,and high energy density have attracted widespread attention. Aramid nanofibers (ANF) were utilized as fiber reinforcement
Externí odkaz:
https://doaj.org/article/8d482b34a7e9466789d45a07a177aca0