Zobrazeno 1 - 10
of 1 118
pro vyhledávání: '"Duan, Jinqiao"'
Discovering explicit governing equations of stochastic dynamical systems with both (Gaussian) Brownian noise and (non-Gaussian) L\'evy noise from data is chanllenging due to possible intricate functional forms and the inherent complexity of L\'evy mo
Externí odkaz:
http://arxiv.org/abs/2409.19534
The Onsager--Machlup action functional is an important concept in statistical mechanics and thermodynamics to describe the probability of fluctuations in nonequilibrium systems. It provides a powerful tool for analyzing and predicting the behavior of
Externí odkaz:
http://arxiv.org/abs/2409.01340
In this paper, we establish some Strichartz estimates for orthonormal functions and probabilistic convergence of density functions related to compact operators on manifolds. Firstly, we present the suitable bound of $\int_{a\leq|s|\leq b}e^{isx}s^{-1
Externí odkaz:
http://arxiv.org/abs/2408.13764
Autor:
Fang, Cheng, Duan, Jinqiao
Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena mak
Externí odkaz:
http://arxiv.org/abs/2403.17032
Critical transition and tipping phenomena between two meta-stable states in stochastic dynamical systems represents an important problem. In this work, we expand the methodology from the traditional Onsager-Machlup action functional, which typically
Externí odkaz:
http://arxiv.org/abs/2403.10405
In this paper, we first investigate the global existence of a solution for the stochastic fractional nonlinear Schr\"odinger equation with radially symmetric initial data in a suitable energy space $H^{\alpha}$. We then show that the stochastic fract
Externí odkaz:
http://arxiv.org/abs/2401.15608
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or boundi
Externí odkaz:
http://arxiv.org/abs/2401.11261
Solving complex fluid-structure interaction (FSI) problems, characterized by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics (CFD) solvers are insuffici
Externí odkaz:
http://arxiv.org/abs/2401.02311
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we propose a meta learning framework to improve the prediction of early ict
Externí odkaz:
http://arxiv.org/abs/2310.06059
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key technology for privacy-pr
Externí odkaz:
http://arxiv.org/abs/2309.09025