Zobrazeno 1 - 10
of 837
pro vyhledávání: '"SHULMAN, DAVID"'
Autor:
Shulman, David, Dattner, Itai
This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data by integrating physical laws into deep learning models. The APGNN adaptively balances data-driven and physics-informed
Externí odkaz:
http://arxiv.org/abs/2411.10064
Autor:
Shulman, David, Israeli, Assaf, Botnaro, Yael, Margalit, Ori, Tamir, Oved, Naschitz, Shaul, Gamrasni, Dan, Shir, Ofer M., Dattner, Itai
We present an innovative approach leveraging Physics-Guided Neural Networks (PGNNs) for enhancing agricultural quality assessments. Central to our methodology is the application of physics-guided inverse regression, a technique that significantly imp
Externí odkaz:
http://arxiv.org/abs/2403.08653
A Theoretical Investigation of Magnetic Susceptibility Measurement Using Mach-Zehnder interferometer
Autor:
Shulman, David
In this study, we present a novel method for measuring the magnetic susceptibility of liquids using a Mach-Zehnder interferometer. The proposed technique employs a ring magnet to deform the liquid, while a laser beam passes through the liquid to meas
Externí odkaz:
http://arxiv.org/abs/2302.13373
Autor:
Shulman, David
Measuring the magnetic field of permanent magnets can be challenging, but recent research has demonstrated the potential of using deformed diamagnetic liquids to estimate the magnetic field. In this paper, we explore two methods for measuring the mag
Externí odkaz:
http://arxiv.org/abs/2302.11635
Autor:
Shulman, David
In recent years, deep learning has achieved remarkable success in various fields such as image recognition, natural language processing, and speech recognition. The effectiveness of deep learning largely depends on the optimization methods used to tr
Externí odkaz:
http://arxiv.org/abs/2302.09566
Autor:
Shulman, David
Measuring instruments are vital for obtaining accurate data in various fields, including scientific research, engineering, and manufacturing. However, data acquisition can be challenging, particularly when direct cable connections are not feasible or
Externí odkaz:
http://arxiv.org/abs/2302.07091