Implementation of skeletal muscle model with advanced activation control

Autor: Kocková H., Cimrman R.

Publikováno v: Applied and Computational Mechanics, Vol 3, Iss 2, Pp 305-318 (2009)

The paper summarizes main principles of an advanced skeletal muscle model. The proposed mathematical model is suitable for a 3D muscle representation. It respects the microstructure of the muscle which is represented by three basic components: active

Externí odkaz: https://doaj.org/article/4dcb2599c31c4e03be529a9b1b28a7bc

Three-phase phononic materials

Autor: Rohan E., Cimrman R.

Publikováno v: Applied and Computational Mechanics, Vol 3, Iss 1, Pp 5-16 (2009)

We consider a strongly heterogeneous material consisting of three phases: an elastic matrix, medium-size inclusions periodically embedded in the elastic matrix; these inclusions are constituted by small rigid inclusions coated by a very compliant mat

Externí odkaz: https://doaj.org/article/667f704bb8fe495c943579c09d37a7cf

On shape optimization of conduits with incompressible flow

Autor: Cimrman R., Rohan E.

Publikováno v: Applied and Computational Mechanics, Vol 1, Iss 2, Pp 393-400 (2007)

In this paper we present an optimal flow problem in the context of internal laminar incompressible flows of Newtonian liquid. The flow optimization problem is introduced in variational form of the stationary Navier-Stokes equations. The parametrizati

Externí odkaz: https://doaj.org/article/f24fe75d62cd4bda86395cf861a78452

Multiscale modeling of a fluid saturated medium with double porosity: Relevance to the compact bone

Autor: Rohan, E., Naili, S., Cimrman, R., Lemaire, T.

Publikováno v: In Journal of the Mechanics and Physics of Solids May 2012 60(5):857-881

SciPy 1.0: fundamental algorithms for scientific computing in Python: 24 February 2020 : An amendment to this paper has been published and can be accessed via a link at the top of the paper

Autor: Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, I., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., Mulbregt, P., Vijaykumar, A., Bardelli, A.P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C.N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D.A., Hagen, D.R., Pasechnik, D.V., Olivetti, E., Martin, E., Wieser, E., Lenders, F., Silva, Fabrice, Wilhelm, F., Young, G., Price, G.A., Ingold, G.-L., Allen, G.E., Lee, G.R., Audren, H., Probst, Irvin, Dietrich, J.P., Silterra, J., Webber, J.T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J.L., Miranda Cardoso, J.V., Reimer, J., Harrington, J., Rodríguez, J.L.C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N.J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P.A., Lee, P., McGibbon, R.T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T.J., Robitaille, T.P., Spura, T., Jones, T.R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y.O., Vázquez-Baeza, Y., SciPy 1.0, Contributors

Publikováno v: Nature Methods
Nature Methods, 2020, 17, pp.261-272. ⟨10.1038/s41592-019-0686-2⟩
Virtanen, P, Gommers, R, Oliphant, T E, Haberland, M, Reddy, T, Cournapeau, D, Burovski, E, Peterson, P, Weckesser, W, Bright, J, Van Der Walt, S J, Brett, M, Wilson, J, Millman, K J, Mayorov, N, Nelson, A R J, Jones, E, Kern, R, Larson, E, Carey, C J, Polat, I, Feng, Y, Moore, E W, Vanderplas, J, Laxalde, D, Perktold, J, Cimrman, R, Henriksen, I, Quintero, E A, Harris, C R, Archibald, A M, Ribeiro, A H, Pedregosa, F, Van Mulbregt, P & Tygier, S 2020, ' SciPy 1.0: fundamental algorithms for scientific computing in Python ', Nature Methods . https://doi.org/10.1038/s41592-019-0686-2

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdc72e11bb5b5dd5b8be9cd6adc0e9b9
https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/69920