Results 71 to 80 of about 156,184 (217)

The renormalization group via statistical inference

New Journal of Physics, 2015
In physics, one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally.
Cédric Bény, Tobias J Osborne
doaj   +1 more source

On a realization of {β}-expansion in QCD

Journal of High Energy Physics, 2017
We suggest a simple algebraic approach to fix the elements of the {β}-expansion for renormalization group invariant quantities, which uses additional degrees of freedom.
S.V. Mikhailov
doaj   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Schwinger-Dyson renormalization group [PDF]

Physical Review B, 2013
We use the Schwinger-Dyson equations as a starting point to derive renormalization flow equations. We show that Katanin's scheme arises as a simple truncation of these equations. We then give the full renormalization group equations up to third order in the irreducible vertex.
Veschgini, Kambis, Salmhofer, Manfred
openaire   +2 more sources

Lung maneuvers of large amplitudes for probing physiological alterations in mouse models of asthma

Animal Models and Experimental Medicine, EarlyView.
Lung maneuvers of small amplitudes, such as the ones performed in oscillometry, are the current gold standard to probe lung mechanics in animals, but they are insensitive to detect baseline physiological alterations in mouse models of asthma. Herein, lung maneuvers of different amplitudes were compared in two mouse models of asthma.
Magali Boucher, Cyndi Henry, Marie‐Josée Beaulieu, Andrés Rojas‐Ruiz, Ynuk Bossé   +4 more
wiley   +1 more source

A Renormalization Group Procedure for Fiber Bundle Models

Frontiers in Physics, 2018
We introduce two versions of a renormalization group scheme for the equal load sharing fiber bundle model. The renormalization group is based on formulating the fiber bundle model in the language of damage mechanics.
Srutarshi Pradhan, Alex Hansen, Purusattam Ray, Purusattam Ray   +3 more
doaj   +1 more source

Gravitational Dressing of Renormalization Group

, 1993
We study the gravitational dressing of renormalizable two-dimensional field theories. Our main result is that the one-loop $\beta$-function is finitely renormalized by the factor ${k+2\over k+1}$, where $k$ is the central charge of the gravitational $SL ...
Klebanov, I. R., Kogan, I. I., Polyakov, A. M.   +2 more
core   +1 more source

Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley   +1 more source

Low-energy theorem revisited and OPE in massless QCD

Journal of High Energy Physics
We revisit a low-energy theorem (LET) of NSVZ type in SU(N) QCD with N f massless quarks derived in [1] by implementing it in dimensional regularization. The LET relates n-point correlators in the l.h.s.
Marco Bochicchio, Elisabetta Pallante
doaj   +1 more source

The density-matrix renormalization group

, 2004
The density-matrix renormalization group (DMRG) is a numerical algorithm for the efficient truncation of the Hilbert space of low-dimensional strongly correlated quantum systems based on a rather general decimation prescription.
Akutsu, N., Akutsu, N., Ammon, B., Ammon, B., Anderson, P. W., Andersson, M., Batista, C. D., Baxter, R. J., Bedürftig, G., Boman, M., Bursill, R. J., Carlon, E., Carruzo, H. M., Chernychev, A. L., Cullum, J., Cuthill, E., Daul, S., Dimitrova, S. S., Dukelsky, J., Enss, T., Foster, D. P., Foster, D. P., Gobert, D., Golub, G., Guerrero, M., Hallberg, K., Hallberg, K., Hida, K., Hida, K., Hida, K., Hida, K., Hida, K., Hikihara, T., Hikihara, T., Hikihara, T., Jeckelmann, E., Jeckelmann, E., Jeckelmann, E., Jeckelmann, E., Jeckelmann, E., Juozapavicius, A., Kolezhuk, A. K., Kühner, T. D., Latorre, J. I., Laukamp, M., Legeza, Ö., Legeza, Ö., Legeza, Ö., Legeza, Ö., Liu, J., Lou, J. Z., Maurel, P., McCulloch, I. P., McCulloch, I. P., Meden, V., Meden, V., Mutou, T., Mutou, T., Ng, T. K., Nielsen, M. A., Nishiyama, Y., Nishiyama, Y., Noack, R. M., Noack, R. M., Okunishi, K., Okunishi, K., Otsuka, H., Paeßens, M., Pang, H. B., Pati, S. K., Pati, S. K., Pati, S. K., Pittel, S., Qin, S. J., Qin, S. J., Rommer, S., Sandvik, A. W., Sato, R., Scalapino, D. J., Schmitteckert, P., Schollwöck, U., Shibata, N., Shibata, N., Shibata, N., Shibata, N., Shibata, N., Shibata, N., Shuai, Z., Shuai, Z., Silva-Valencia, J., Sirker, J., Sirker, J., Sugihara, T., Sørensen, E., Sørensen, E., van Kampen, N. G., Wang, X. Q., Wegner, F., White, S. R., White, S. R., White, S. R., White, S. R., White, S. R., White, S. R., White, S. R., White, S. R., Xiang, T., Yamamoto, T., Yamashita, K., Yamashita, Y., Zhu, N. S.   +110 more
core   +2 more sources