Brian Neal Granzow

Computational Multiphysics

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Computational Multiphysics

(505) 284-3216

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1321


Brian received a Ph.D. in mechanical engineering from Rensselaer Polytechnic Institute and a B.S. in applied mathematics from the University of New Mexico. His research interests include developing numerical algorithms and software for predictive simulation on next-generation architectures, a posteriori error estimation in finite element analysis, unstructured mesh adaptivity, and adaptive mesh mesh refinement.


B.S. Applied Mathematics – University of New Mexico

Ph.D. Mechanical Engineering – Rensselaer Polytechnic Institute


Daniel Thomas Seidl, Brian Neal Granzow, (2022). Automatic Differentiation-based Approaches to Constitutive Model Calibration and Goal-oriented Error Estimation for Computational Solid Mechanics Boston University Seminar Document ID: 1675650

Daniel Thomas Seidl, Brian Neal Granzow, (2022). Calibration of Elastoplastic Constitutive Model Parameters with Automatic Differentiation-based Sensitivities: Application to Full-field Experimental Data Annual International DIC Conference Document ID: 1675517

Thomas E. Voth, Brian Neal Granzow, (2022). ALEGRA 2D Contact/Impact; AWE Review AWE-SNL ALEGRA Meeting Document ID: 1619075

John Henry Judah Niederhaus, Michael Powell, Steven W. Bova, James Brian Carleton, John H. Carpenter, Kyle Cochrane, Michael Meyers Crockatt, Wen Dong, Timothy Jesse Fuller, Brian Neal Granzow, Daniel Alejandro Ibanez-Granados, Stephen Ray Kennon, Christopher Bernard Luchini, Ramon Jules Moral, Christopher John O’Brien, Allen C. Robinson, Angel Eliud Rodriguez, Jason James Sanchez, Walter Alan Scott, Christopher Siefert, Alan K. Stagg, Irina Kalashnikova Tezaur, Thomas E. Voth, (2022). ALEGRA: finite element modeling for shock hydrodynamics and multiphysics Hypervelocity Impact Symposium 2022 Document ID: 1629546

James W. Foulk, Andrew Ackerman, Ashley C. Fate, Terri L. Galpin, Brian Neal Granzow, Martin W. Heinstein, David R. Noble, Tyler Shelton, Matthew L. Staten, Christopher Riley Wilson, Justin Winokur, Gregg Whitford, (2022). Aiding the design and analyst communities through automation for rapid decision making Workshop with UCSD Document ID: 1618303

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