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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
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Operations Research, 2021
Decision making under uncertainty involves both ambiguity and risk. In “Robust CARA Optimization,” Chen and Sim have developed innovative optimization models designed for ambiguity-averse decision makers whose risk preference is consistent with constant absolute risk aversion (CARA).
Li Chen, Melvyn Sim
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Decision making under uncertainty involves both ambiguity and risk. In “Robust CARA Optimization,” Chen and Sim have developed innovative optimization models designed for ambiguity-averse decision makers whose risk preference is consistent with constant absolute risk aversion (CARA).
Li Chen, Melvyn Sim
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Robustness, Optimization, and Architectures
European Journal of Control, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lijun Chen 0001 +3 more
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An uncertain minimization problem: robust optimization versus optimization of robustness
Optimization Letters, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Moussa Barro, Sado Traoré
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Mathematics of Operations Research, 1998
We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Aharon Ben-Tal, Arkadi Nemirovski
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We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization.
Aharon Ben-Tal, Arkadi Nemirovski
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Robust Multiobjective Optimization With Robust Consensus
IEEE Transactions on Fuzzy Systems, 2018Consider a multiobjective robust optimization problem, where a set of weighted decision makers provides their preferences a priori . The preferences are provided either in the objective space or in the decision variable space using fuzzy numbers. To solve this problem, an indicator to measure consensus, an indicator to measure the robustness of the ...
Kaustuv Nag +3 more
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Statistics & Risk Modeling, 1993
Summary: We consider the problem of discrete-time causal filtering for scalar systems in the presence of data outliers. We model the outliers as an extension to time-series of Huber's \(\varepsilon\)-contamination model [\textit{P. J. Huber}, Ann. Math. Statist. 35, 73-101 (1964; Zbl 0136.398); ibid.
Birmiwal, Kailash, Shen, Jun
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Summary: We consider the problem of discrete-time causal filtering for scalar systems in the presence of data outliers. We model the outliers as an extension to time-series of Huber's \(\varepsilon\)-contamination model [\textit{P. J. Huber}, Ann. Math. Statist. 35, 73-101 (1964; Zbl 0136.398); ibid.
Birmiwal, Kailash, Shen, Jun
openaire +2 more sources