Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Tang Sui"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104034- (2024)
The fusion of remote sensing and artificial intelligence, particularly deep learning, offers substantial opportunities for developing innovative methods in rapid disaster mapping and damage assessment. However, current models for wildfire burnt area
Externí odkaz:
https://doaj.org/article/f1273f6160ef470797f86c29fa014b21
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17187-17206 (2024)
Wildfires cause substantial damage and present considerable risks to both natural ecosystem and human societies. A precise and prompt evaluation of wildfire-induced damage is crucial for effective postfire management and restoration. Considerable adv
Externí odkaz:
https://doaj.org/article/39f8319e483c4672a026b09f36e0952e
In this work, we investigate the sampling and reconstruction of spectrally $s$-sparse bandlimited graph signals governed by heat diffusion processes. We propose a random space-time sampling regime, referred to as {randomized} dynamical sampling, wher
Externí odkaz:
http://arxiv.org/abs/2410.18005
In this work, we explore the dynamical sampling problem on $\ell^2(\mathbb{Z})$ driven by a convolution operator defined by a convolution kernel. This problem is inspired by the need to recover a bandlimited heat diffusion field from space-time sampl
Externí odkaz:
http://arxiv.org/abs/2406.15122
We address the inverse problem of identifying nonlocal interaction potentials in nonlinear aggregation-diffusion equations from noisy discrete trajectory data. Our approach involves formulating and solving a regularized variational problem, which req
Externí odkaz:
http://arxiv.org/abs/2402.06355
In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and collective behavior of interacting agents of similar size and body type. T
Externí odkaz:
http://arxiv.org/abs/2311.00902
Publikováno v:
Siam Journal on Applied Math 2024
In this paper, we tackle a critical issue in nonparametric inference for systems of interacting particles on Riemannian manifolds: the identifiability of the interaction functions. Specifically, we define the function spaces on which the interaction
Externí odkaz:
http://arxiv.org/abs/2305.12340
We consider the nonlinear inverse problem of learning a transition operator $\mathbf{A}$ from partial observations at different times, in particular from sparse observations of entries of its powers $\mathbf{A},\mathbf{A}^2,\cdots,\mathbf{A}^{T}$. Th
Externí odkaz:
http://arxiv.org/abs/2212.00746
The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve policy making. The extremely large scale of
Externí odkaz:
http://arxiv.org/abs/2211.17014
Large-scale dynamics of the oceans and the atmosphere are governed by primitive equations (PEs). Due to the nonlinearity and nonlocality, the numerical study of the PEs is generally challenging. Neural networks have been shown to be a promising machi
Externí odkaz:
http://arxiv.org/abs/2209.11929