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Project starting date: January 1st, 2010
Research / Job description
Fast and accurate simulation of deformable material is a long standing goal across a variety of disciplines ranging from engineering to feature film industry. Despite great strides, physical simulation is still impeded by long design cycles requiring both intuition and technical expertise. Furthermore, as we cannot account for every material property, every interaction, and every particular environment, the scalability of existing techniques poses great challenges. This research seeks to overcome these obstacles with novel methods that combine acquisition techniques, differential geometry, and mechanics to develop novel simulation models that accurately reproduce realistic deformation behavior. Traditionally, physical assumptions are synthesized into the modeling process by projecting the constitutive equations and energy balance relations onto a priori chosen set of basis functions (e.g. polynomials in the finite element method). This choice of basis can have deep implications on the simulation. Indeed, major challenges such as spurious energy, locking, and hourglass effect can be attributed to inadequacy of the basis. The scientific objective of this project is to develop new deformation models, where basis functions and material behavior are adaptively learned from acquisition, and thus have inherently a clear physical meaning. In this way, the simulation goes on par with the real deformation behavior and the above mentioned problems are avoided in the first place. The PhysiGrafix project consists of three major tasks i. Develop efficient techniques for non-intrusive (e.g. optical) tracking and capturing of relevant geometric and physical properties of deformable material and reconstruction of a coarse representation of the deformation sequence; ii. Design new discrete deformation models where deformation behavior is fully learned from acquisition; with special focus on problem reduction by encoding the physics in relevant deformation modes and elimination of irrelevant parameters (e.g. rigid body modes); iii. Adaption of the simulation to refined reconstruction as well as to further acquisition data. PhD candidate #1 will focus on task (i), and PhD candidate #2 will address task (ii). Task (iii) will require the joint effort of both candidates. Required qualifications
The successful candidates will have a Masters degree or equivalent in Computer Science, Physics, Mathematics or Engineering, ideally with experience in programming and interest in interdisciplinary research at the interface between geometry, vision, and mechanics.
Terms of employment
The positions are intended as full-time, 36 months appointment funded through the “Chairs of Excellence” program of the ANR( Agence National de la Recheche). After successful completion of the PhD research a PhD degree will be granted. Working language is English. French language courses are also available for foreign students. For further information please contact:
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Phone: +33 383-592 093 INRIA ALICE Campus scientifique
615, rue du Jardin Botanique,
54600, Villers les Nancy
France About the group ALICE |
