My research interests have long focused on the development of reliable and efficient computer algorithms for the prediction of physical phenomena as encountered in solid and fluid mechanics. In particular, I have contributed to the development of so-called goal-oriented error estimators for discretization error and corresponding adaptive approaches. The novel idea in these methods is to control the discretization errors, in finite element approximations for example, with respect to quantities of interest rather than in terms of abstract global norms. These error estimation techniques were later extended to the control of modeling errors in situations where base models, believed to capture events of interest but intractable, are replaced by surrogate models, tractable but designed to capture only the coarse scales of the physical phenomena. The methodology has recently been applied to the simulation of atomic-to-continuum coupling methods and allow to optimally determine the location of the interface separating the regions in which the molecular model and a continuum model are used. I am also interested in Verification & Validation and Uncertainty Quantification for predictive simulation-based engineering and science. Other research interests include, for example, the analysis of large eddy simulation models for fluid flow turbulence, diffuse-interface phase field modeling for tumor growth, or uncertainty quantification for models of porous media, etc.
J. T. Oden and S. Prudhomme. Goal-oriented error estimation and adaptivity for the finite element method. Computers Math. Applications, 41(5–6):735–756, 2001.
J. T. Oden and S. Prudhomme. Estimation of modeling error in computational mechanics. Journal of Computational Physics, 182:496–515, 2002.
P. T. Bauman, J. T. Oden, and S. Prudhomme. Adaptive multiscale modeling of polymeric materials: Arlequin coupling and goals algorithms. Computer Methods in Applied Mechanics and Engineering, 198:799–818, 2009.
M. Panesi, K. Miki, S. Prudhomme, and A. Brandis, On the assessment of a Bayesian validation methodology for data reduction models relevant to shock tube experiments, Computer Methods in Applied Mechanics and Engineering, Vol. 213–216, 383–398 (2012).
R. Morrison, C. Bryant, G. Terejanu, K. Miki, and S. Prudhomme, Optimal data split methodology for model validation, Computer & Mathematics with Applications, Submitted (2012).
Ph.D. in Aerospace Engineering, UT Austin (1999)
M.Sc. in Mechanical Engineering, UVA (1992)
Diploma of Engineering, Ecole Centrale de Lille (1991)
Scientific and Professional Membership
USACM, IACM, SIAM
SRI UQ Center
Department of Mathematics and industrial engineering, Ecole Polytechnique de Montréal
Institute for Computational Engineering and Sciences, The University of Texas at Austin
Research Interests Keywords
Computational engineering and scienceFinite Element methodsA posteriori error estimation and adaptive methodsMultiscale modeling, Verification and Validation