Zobrazeno 1 - 10
of 741
pro vyhledávání: '"Nagata, KENji"'
The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a revolution in research methodology. Our group has proposed Bayesian meas
Externí odkaz:
http://arxiv.org/abs/2406.02869
Autor:
Nagata, Kenji, Mototake, Yoh-ichi
The Metropolis algorithm is one of the Markov chain Monte Carlo (MCMC) methods that realize sampling from the target probability distribution. In this paper, we are concerned with the sampling from the distribution in non-identifiable cases that invo
Externí odkaz:
http://arxiv.org/abs/2406.00369
Autor:
Oyama, Keigo, Hayashi, Yui, Kuwamoto, Shigeo, Katakami, Shun, Nagata, Kenji, Mizumaki, Masaichiro, Okada, Masato
Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physics. During the analysis of SAS data, model parameters that contain info
Externí odkaz:
http://arxiv.org/abs/2405.07302
Autor:
Shieh, Binsheu, Masuda, Ryo, Tsutsui, Satoshi, Katakami, Shun, Nagata, Kenji, Mizumaki, Masaichiro, Okada, Masato
M\"ossbauer spectroscopy is a technique employed to investigate the microscopic properties of materials using transitions between energy levels in the nuclei. Conventionally, in synchrotron-radiation-based M\"ossbauer spectroscopy, the measurement wi
Externí odkaz:
http://arxiv.org/abs/2404.13916
Autor:
Murakami, Ryo, Sasaki, Taisuke T., Yoshikawa, Hideki, Matsushita, Yoshitaka, Sodeyama, Keitaro, Ohkubo, Tadakatsu, Shinotsuka, Hiroshi, Nagata, Kenji
To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray diffracti
Externí odkaz:
http://arxiv.org/abs/2403.09677
Autor:
Hayashi, Yui, Katakami, Shun, Kuwamoto, Shigeo, Nagata, Kenji, Mizumaki, Masaichiro, Okada, Masato
Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypoth
Externí odkaz:
http://arxiv.org/abs/2401.10466
Crystalline phase structure is essential for understanding the performance and properties of a material. Therefore, this study identified and quantified the crystalline phase structure of a sample based on the diffraction pattern observed when the cr
Externí odkaz:
http://arxiv.org/abs/2309.14785
In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility an
Externí odkaz:
http://arxiv.org/abs/2305.07040
Autor:
Hayashi, Yui, Katakami, Shun, Kuwamoto, Shigeo, Nagata, Kenji, Mizumaki, Masaichiro, Okada, Masato
In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve processes of manual parameter adjustment by analysts or optimization us
Externí odkaz:
http://arxiv.org/abs/2303.04983
In this paper, we propose a Bayesian spectral deconvolution method for absorption spectra. In conventional analysis, the noise mechanism of absorption spectral data is never considered appropriately. In that analysis, the least-squares method, which
Externí odkaz:
http://arxiv.org/abs/2212.07053