Zobrazeno 1 - 10
of 474
pro vyhledávání: '"Wang, SiFan"'
We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures. Disk2Planet takes as input the disk structures in the form of two-dimensional density and velocity ma
Externí odkaz:
http://arxiv.org/abs/2409.17228
Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Microm
Externí odkaz:
http://arxiv.org/abs/2410.05281
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, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer
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