View Picture Gallery
Watch the Lecture

The material models used in engineering design are often a major source of uncertainty in the quantification of performance margins. Traditionally, computer codes were baselined against relevant full-system data and combined with many simplifying assumptions. The resulting empirical models were interpolative in nature and could not reliably be applied outside the range of the calibration data set. Likewise, current engineering material models are empirical in nature and often result in unacceptable levels of uncertainty in calculated performance margins. Worse still, the phenomenological approach does not offer a systematic means of increasing fidelity − and reducing uncertainty − in material modeling. Thus, the introduction of more complex ad hoc response functions invariably introduces additional empirical constants, often without a clear physical meaning, which themselves become a new source of uncertainty. The problem is often compounded by the need to describe material behavior under extreme conditions of strain rate, deformation, temperature and pressure, often outside the range of direct laboratory testing; or the need to deploy structures that cannot be fully tested in the laboratory, such as large-aperture telescopes. Within this context, Multiscale Modelling of Materials (MMM) may be viewed as a paradigm for systematically reducing uncertainty in simulations involving complex material behavior. The ultimate goal of MMM is to enable the simulation of full-scale systems without empirical parameters or phenomenological relations, i.e., on the sole basis of fundamental theories such as quantum mechanics, and in this sense it is at the core of − and a major step towards  − predictive science. The attainability of this ultimate goal notwithstanding, a more immediate and practical goal of MMM is to systematically enhance the fidelity of design codes through the gradual addition of improved physics at all relevant length and time scales. An immediate consequence of the present overarching emphasis on predictive science in general, and on MMM in particular, is that it is forcing a far-reaching reevaluation of the relative − sometimes competing − roles of testing, simulation and certification.

Professor Ortiz received his BS degree in Civil Engineering from the Polytechnic University of Madrid, Spain, and his MS and Ph.D. degrees in Civil Engineering from the University of California at Berkeley.  After serving in the Division of Engineering at Brown University, Prof. Ortiz joined the California Institute of Technology in 1995, where he was named the Dotty and Dick Hayman Professor of Aeronautics and Mechanical Engineering in 2004. He currently leads the Solid Dynamics group of the ASCI/ASAP Center for the Simulation of the Dynamic Response of Materials.  Professor Ortiz has been a Fulbright Scholar, a corresponding member of the Spanish Academy of Engineering, a Sherman Fairchild Distinguished Scholar at Caltech, a Fellow and elected member-at large of the US Association for Computational Mechanics, and the recipient of the 2002 Humboldt Research Award for Senior U.S. Scientists.  Since 2002, Professor Ortiz has served on the University of California Office of the President Science and Technology Panel.