Zobrazeno 1 - 10
of 6 729
pro vyhledávání: '"physics-informed neural network"'
We propose a physics-informed neural network (PINN) model to efficiently predict the self-energy of Anderson impurity models (AIMs) based on the Lehmann representation. As an example, we apply the PINN model to a single-orbital AIM (SAIM) for a nonin
Externí odkaz:
http://arxiv.org/abs/2411.18835
An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumu
Externí odkaz:
http://arxiv.org/abs/2411.14691
Contraction analysis offers, through elegant mathematical developments, a unified way of designing observers for a general class of nonlinear systems, where the observer correction term is obtained by solving an infinite dimensional inequality that g
Externí odkaz:
http://arxiv.org/abs/2411.09237
Autor:
Kütük, Mustafa, Yücel, Hamdullah
This paper investigates a numerical solution of Allen-Cahn equation with constant and degenerate mobility, with polynomial and logarithmic energy functionals, with deterministic and random initial functions, and with advective term in one, two, and t
Externí odkaz:
http://arxiv.org/abs/2411.08760
Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family
Externí odkaz:
http://arxiv.org/abs/2411.06781
Autor:
Bai, Jinshuai, Lin, Zhongya, Wang, Yizheng, Wen, Jiancong, Liu, Yinghua, Rabczuk, Timon, Gu, YuanTong, Feng, Xi-Qiao
Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various conditions. In this work, we propose an energy-based physics-informed neur
Externí odkaz:
http://arxiv.org/abs/2411.03671
We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based topology optimization (TO) algorithm. This approach leverages two inter
Externí odkaz:
http://arxiv.org/abs/2410.14342
For the 3D localization problem using point spread function (PSF) engineering, we propose a novel enhancement of our previously introduced localization neural network, LocNet. The improved network is a physics-informed neural network (PINN) that we c
Externí odkaz:
http://arxiv.org/abs/2410.13295
In this paper, we present a novel approach for fluid dynamic simulations by harnessing the capabilities of Physics-Informed Neural Networks (PINNs) guided by the newly unveiled principle of minimum pressure gradient (PMPG). In a PINN formulation, the
Externí odkaz:
http://arxiv.org/abs/2411.15410
Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications. This task requires not only fitting market data but also satisfying
Externí odkaz:
http://arxiv.org/abs/2411.02375