Multi-Information Source Value of Information Based Design of Multi-Phase Structural Materials
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abstract
The goal of this research project is to create a multi-information source value-of-information based framework for the design of complex, multi-component, multi-phase advanced steels. In materials science a material design problem that exploits the chemistry-processing-microstructure-properties paradigm is often expensive, time consuming and limited by the available resources. Thus, to achieve a design goal, one must carefully select the information source to query, which could be an experiment, a computational simulation, elicitation of expert opinion, etc., based on the value added to the design problem, as well as the time/cost associated with performing the query. The complex microstructures in advanced steels result in significant improvements in strength and ductility compared to conventional steels. This makes such steels especially attractive for automotive applications, where vehicle weight reduction and crashworthiness are paramount. The approach is a completely new and potentially transformative way of looking at materials design in general. Here, experiments, simulations, and prior knowledge are accounted for at the same level, potentially alleviating major drawbacks of established paradigms in that there is no longer any required alignment between inputs and outputs of computational or experimental methods. The information source fidelity is implicitly accounted for, and the design framework can efficiently allocate limited resources to the materials design task in an information-economic sense. The research will also be integrated with the NSF Research and Traineeship Program at Texas A&M and outreach to broader communities through existing programs at Texas A&M. The objective of this research is to exploit the chemistry-processing-microstructure-properties paradigm in materials design within a framework that facilitates the integration of multiple information sources. It prescribes optimal information acquisition protocols that balance exploration and exploitation of the design space. This framework will extend state-of-the-art knowledge gradient policies for value of information based optimal querying of information sources. Key contributions of the framework include enabling parallel querying of correlated information sources by fitting Gaussian process models to learned correlations; incorporation of information sources with differing input/output spaces by leveraging high dimensional model representations; and multi-objective policies that provide scalar indicators of expected improvement through novel importance reweighting of prior queried information into state-of-the-art hypervolume indicators. Contrary to many existing materials design frameworks, the framework directly connects materials chemistry/processing to property, avoiding issues related to discrepancies between optimality and feasibility. The framework will be able to accommodate predictions of mechanical properties with models of different resolution and, more importantly, allows for the integration of experiments and prior knowledge within the same unified framework. Although the demonstration focus is on low alloy multi-phase advanced steels, the methodology is potentially applicable to a wide range of materials systems.
