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It is with great pleasure that we contribute this study in honor of our dear colleague, friend, and visionary in the field of numerical methods in engineering, Professor Ted Belytschko. We thank him for his numerous contributions to this field, for his statesmanship, and for his leadership over the many years of development of the subject of computational(More)
In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experimental data and computational model. The methodology involves (i) data from fatigue tests conducted on coupons of Ti6Al4V material; (ii) continuum damage mechanics based material(More)
This chapter summarizes the results of a feasibility study exploring the development of a stochastic Dynamic Data-Driven Application System (DDDAS) for prediction and monitoring of material damage in composite materials common to many types of contemporary high-performance military aircraft. The methodology involves (1) acquiring data from mechanical(More)
*Correspondence: oden@ices.utexas.edu Institute for Computational Engineering and Sciences The University of Texas at Austin, 201 East 24th St, Stop C0200 POB 4.102, 78712 Austin, TX, USA Abstract Background: The use of coarse-grained approximations of atomic systems is the most common methods of constructing reduced-order models in computational science.(More)
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