Zobrazeno 1 - 10
of 7 137
pro vyhledávání: '"Informed machine learning"'
Twelve physics informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies and three different mass models. Then four machine
Externí odkaz:
http://arxiv.org/abs/2412.09504
With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the temperature of
Externí odkaz:
http://arxiv.org/abs/2411.09915
This work presents a supervised machine-learning (ML) approach for blind digital calibration of SAR ADCs without requiring prior knowledge of errors. A low-speed reference ADC is used to train a shallow neural network (NN) to estimate errors in a hig
Externí odkaz:
http://arxiv.org/abs/2412.14051
Autor:
Rashki, Mohsen
Machine learning (ML) has emerged as a powerful tool for tackling complex regression and classification tasks, yet its success often hinges on the quality of training data. This study introduces an ML paradigm inspired by domain knowledge of the stru
Externí odkaz:
http://arxiv.org/abs/2412.11526
The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML (PINN) model
Externí odkaz:
http://arxiv.org/abs/2411.19031
Traditional rule-based physical models are limited by their reliance on singular physical formulas and parameters, making it difficult to effectively tackle the intricate tasks associated with crowd simulation. Recent research has introduced deep lea
Externí odkaz:
http://arxiv.org/abs/2410.16132
Autor:
Aghaei, Alireza Afzal
This paper introduces a novel methodology for solving distributed-order fractional differential equations using a physics-informed machine learning framework. The core of this approach involves extending the support vector regression (SVR) algorithm
Externí odkaz:
http://arxiv.org/abs/2409.03507
The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, which is the basis for various applied technologies related to spatial a
Externí odkaz:
http://arxiv.org/abs/2408.14731
Publikováno v:
Defence Technology, Vol 41, Iss , Pp 119-133 (2024)
The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of in
Externí odkaz:
https://doaj.org/article/0f5da5cadf9a41ed8b3182c1d37924c0
Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or the
Externí odkaz:
http://arxiv.org/abs/2410.17587