Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

Abstract

Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and 'ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel valuegradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over * Address all correspondence to this author.the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strengthnormalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources-i.e. reduced order models-based on different homogenization assumptionsisostrain, isostress and isowork-are used to efficiently and optimally query the materials design space. arXiv:1809.09686v1 [physics.app-ph]

Bayesian optimization with active learning of design constraints using an entropy-based approach

“…Following refs. 15,16 , we formulate the surrogates (GPRs) by assuming we have available multiple information sources, f i (x), where i ∈ {1, 2, …, S}, to estimate a quantity of interest, f(x), at design point x. These surrogates are indicated by f GP,i (x).…”

“…An improvement to the Bayesian optimization paradigm is to employ multiple models representing the same quantity of interest. This is known as multi-fidelity BO and has been shown to effectively increase the robustness and efficiency of engineering design schemes [12][13][14][15][16] . These models are built upon different assumptions and/or simplifications and vary in fidelity and cost of the evaluation.…”

“…In the earlier works of refs. [12][13][14][15][16] , a multi-fidelity approach has been employed to optimize a single quantity of interest (single-objective optimization). Recently, this multi-fidelity setting has been expanded to multi-objective design problems as well 17 .…”

Uncertainty analysis of microsegregation during laser powder bed fusion

“…One direction in the literature to improve the prediction of the surrogate model is to construct it using a combination of high-fidelity and low-fidelity simulations, rather than only using high-fidelity simulations. [66,67]. In this sense, the process would maximize the information gained on every query to the expensive full model.…”

“…Further, to optimally improve the predictive capability of the GP surrogate model over the domain we could develop sequential full model sampling policies based on maximizing the Kullback-Liebler divergence (Eq. ( 8)) between the current surrogate model and a surrogate model with one extra data point [66,67]. In this sense, the process would maximize the information gained on every query to the expensive full model.…”