Zobrazeno 1 - 10
of 84
pro vyhledávání: '"H. Teichert"'
Publikováno v:
Archives of Computational Methods in Engineering
We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) version of the Susceptible, Infected, Recovered, Deceased (SIRD) compartm
Publikováno v:
Computational Mechanics. 66:1153-1176
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment
Publikováno v:
Proposed for presentation at the 16th U.S. National Congress on Computational Mechanics held July 25-29, 2021 in Chicago, Illinois..
Material produced by current metal additive manufacturing processes is susceptible to variable performance due to imprecise control of internal porosity, surface roughness, and conformity to designed geometry. Using a double U-notched specimen, we in
Autor:
Brian Puchala, A. D. Murphy, Larry K. Aagesen, Jason Luce, Katsuyo Thornton, Zhe Chen, Liang Qi, T. D. Berman, Zhenlin Wang, K. Sagiyama, Emmanuelle A. Marquis, Anirudh Raju Natarajan, Shardul S. Panwar, Vikram Gavini, T. Weymouth, Shiva Rudraraju, Zhihua Huang, W. B. Andrews, Chaoming Yang, H. V. Jagadish, Gregory H. Teichert, David Montiel, J. W. Jones, John C. Thomas, Vicente Araullo-Peters, Krishna Garikipati, Veera Sundararaghavan, Samantha Daly, Sriram Ganesan, A. Githens, Margaret Hedstrom, Phani Motamarri, J. F. Adams, G. Tarcea, John E. Allison, A. Van der Ven, Amit Misra, Stephen DeWitt, Sambit Das, Ellen L.S. Solomon
Publikováno v:
JOM. 70:2298-2314
The Center for Predictive Integrated Structural Materials Science (PRISMS Center) is creating a unique framework for accelerated predictive materials science and rapid insertion of the latest scientific knowledge into next-generation ICME tools. Ther
Publikováno v:
Journal of the Mechanics and Physics of Solids. 99:338-356
We present a unified variational treatment of evolving configurations in crystalline solids with microstructure. The crux of our treatment lies in the introduction of a vector configurational field. This field lies in the material, or configurational
Autor:
Anirudh Raju Natarajan, Krishna Garikipati, Brian Puchala, Gregory H. Teichert, Shiva Rudraraju, N. S. Harsha Gunda, Anton Van der Ven
Publikováno v:
Computational Materials Science. 128:127-139
Free energies play a central role in many descriptions of equilibrium and non-equilibrium properties of solids. Continuum partial differential equations (PDEs) of atomic transport, phase transformations and mechanics often rely on first and second de
The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc03296ac5bab339c99d3a08b2793e6d
Autor:
Nathan M. Heckman, Francesco Rizzi, Reese E. Jones, Mohammad Khalil, Jakob T. Ostien, Gregory H. Teichert, Jeremy Alan Templeton, Kousuke Tachida, Ari Frankel, Brad L. Boyce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9079d1d3d6cd6e33a2a0b891fee515e0
https://doi.org/10.2172/1814062
https://doi.org/10.2172/1814062
Autor:
Gregory H. Teichert, Nathan M. Heckman, Mohammad Khalil, Reese E. Jones, Krishna Garikipati, Coleman Alleman, Brad L. Boyce
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 373:113471
To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded
The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this work, we present an Integrable Deep Neural Network (IDNN) that can be tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::871154a74a3d321683a755d42891d430
http://arxiv.org/abs/1901.00081
http://arxiv.org/abs/1901.00081