The objective of this EArly-concept Grant for Exploratory Research (EAGER) project is to apply High Dimensional Model Representation (HDMR) technique to construct highly accurate and efficient surrogate computational mechanics models. Advanced computational mechanics simulations typically deal with very large scale models that are extremely time consuming. HDMR technique uses systemic sampling procedure relating outputs of the original physical model to its inputs to produce highly accurate and computationally cheap equivalent models. These low order surrogate models are expected to dramatically reduce the cost of very large scale simulations while maintaining the accuracy of the original models. The proposed methodology is extendable to other fields of engineering besides computational mechanics.
This research has the potential to transform current state of modeling philosophy and simulation capability in computational mechanics, and enable very large scale simulations at a reasonable cost. Simulations hitherto intractable due to very high computational burden can be performed efficiently with the proposed methodology. Additionally, it will provide new avenues in the development of accurate yet efficient simulation methods. Educational objectives will focus on (1) training graduate as well as undergraduate students in the area of surrogate modeling, and (2) developing new course materials for the existing courses on computational mechanics. The research being at the confluence of diverse areas of science and engineering such as mechanics, statistics and computational methods, will provide rich educational and research experiences for undergraduate and graduate students in state-of-the-art computational modeling and simulation.
The objective of this Early-concept Grant for Exploratory Research (EAGER) project was to apply High Dimensional Model Representation (HDMR) technique to construct highly accurate and efficient surrogate computational mechanics models. Advanced computational mechanics simulations typically deal with very large scale models that are extremely time consuming. HDMR technique is a systemic sampling procedure relating outputs of the original physical model to its inputs to produce highly accurate and computationally cheap equivalent models. These low order surrogate models are expected to dramatically reduce the cost of very large scale simulations while maintaining the accuracy of the original models. A chief benefit of the developed methodology is that it treats the actual physical model as a black box, and thus is readily extendable to other fields of engineering besides computational mechanics. This research has the potential to transform current state of modeling philosophy and simulation capability in computational mechanics, and enable very large scale simulations at a reasonable cost. Simulations hitherto intractable due to very high computational burden can be performed efficiently with the proposed methodology. Additionally, it will provide new avenues in the development of accurate yet efficient simulation methods. Educational objective of the project focused on (1) training one graduate in the general area of computational mechanics, and particularly the area of surrogate modeling, and (2) developing simulation software for undergraduate education. This software was used to demonstrate to the students mechanical response of complex materials in real time. The research being at the confluence of diverse areas of science and engineering such as mechanics, statistics and computational methods, provided rich educational and research experiences for undergraduate and graduate students in state-of-the-art computational modeling and simulation.
