Edited by Dr.Douglas Allaire, 2016, 2017 Edited by Kaiyu Li, 2016, 2017 Edited by Benson Isaac, 2018, 2019 Edited by Douglas Allaire, 2022. Introduction Welcome to the Computational Design Laboratory website. We are in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University in the College of Engineering, and a part of the Engineering Systems Design Group. Research Overview Our research focuses on advancing fundamental computational methodology for the design, analysis, and operation of complex engineered systems. We are currently using our tools to address challenges in enabling self-aware unmanned aerial vehicles and their use within intelligent sensor swarms, exploiting the breadth of available information sources for conceptual design of advanced aircraft, predictive analytics for gas turbine engines using machine learning, and the autonomous design and discovery of materials and material systems. Featured Publications Jakeman, John; Friedman, Sam; Eldred, Michael; Tamellini, Lorenzo; Gorodetsky, Alex; Allaire, DouglasAdaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems Journal Article In: International Journal for Numerical Methods in Engineering, vol. 123, iss. 12, pp. 2760-2790, 2022.Abstract | Links | BibTeX@article{jakeman2022adaptive, title = {Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems}, author = {John Jakeman and Sam Friedman and Michael Eldred and Lorenzo Tamellini and Alex Gorodetsky and Douglas Allaire}, doi = {10.1002/nme.6958}, year = {2022}, date = {2022-06-30}, urldate = {2022-06-30}, journal = {International Journal for Numerical Methods in Engineering}, volume = {123}, issue = {12}, pages = {2760-2790}, abstract = {We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. The multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.}, keywords = {}, pubstate = {published}, tppubtype = {article} } CloseWe present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. The multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.Closedoi:10.1002/nme.6958Close Zhang, Guanglu; Allaire, Douglas; Cagan, JonathanReducing the Search Space for Global Minimum: A Focused Regions Identification Method for Least Squares Parameter Estimation in Nonlinear Models Journal Article In: ASME Journal of Computing and Information Science in Engineering, vol. 23, iss. 2, pp. 021006, 2022.Abstract | Links | BibTeX@article{zhang2022reducing, title = {Reducing the Search Space for Global Minimum: A Focused Regions Identification Method for Least Squares Parameter Estimation in Nonlinear Models}, author = {Guanglu Zhang and Douglas Allaire and Jonathan Cagan}, doi = {doi.org/10.1115/1.4054440}, year = {2022}, date = {2022-06-03}, urldate = {2022-06-03}, journal = {ASME Journal of Computing and Information Science in Engineering}, volume = {23}, issue = {2}, pages = {021006}, abstract = {Important for many science and engineering fields, meaningful nonlinear models result from fitting such models to data by estimating the value of each parameter in the model. Since parameters in nonlinear models often characterize a substance or a system (e.g., mass diffusivity), it is critical to find the optimal parameter estimators that minimize or maximize a chosen objective function. In practice, iterative local methods (e.g., Levenberg\textendashMarquardt method) and heuristic methods (e.g., genetic algorithms) are commonly employed for least squares parameter estimation in nonlinear models. However, practitioners are not able to know whether the parameter estimators derived through these methods are the optimal parameter estimators that correspond to the global minimum of the squared error of the fit. In this paper, a focused regions identification method is introduced for least squares parameter estimation in nonlinear models. Using expected fitting accuracy and derivatives of the squared error of the fit, this method rules out the regions in parameter space where the optimal parameter estimators cannot exist. Practitioners are guaranteed to find the optimal parameter estimators through an exhaustive search in the remaining regions (i.e., focused regions). The focused regions identification method is validated through two case studies in which a model based on Newton’s law of cooling and the Michaelis\textendashMenten model are fitted to two experimental data sets, respectively. These case studies show that the focused regions identification method can find the optimal parameter estimators and the corresponding global minimum effectively and efficiently.}, keywords = {}, pubstate = {published}, tppubtype = {article} } CloseImportant for many science and engineering fields, meaningful nonlinear models result from fitting such models to data by estimating the value of each parameter in the model. Since parameters in nonlinear models often characterize a substance or a system (e.g., mass diffusivity), it is critical to find the optimal parameter estimators that minimize or maximize a chosen objective function. In practice, iterative local methods (e.g., Levenberg–Marquardt method) and heuristic methods (e.g., genetic algorithms) are commonly employed for least squares parameter estimation in nonlinear models. However, practitioners are not able to know whether the parameter estimators derived through these methods are the optimal parameter estimators that correspond to the global minimum of the squared error of the fit. In this paper, a focused regions identification method is introduced for least squares parameter estimation in nonlinear models. Using expected fitting accuracy and derivatives of the squared error of the fit, this method rules out the regions in parameter space where the optimal parameter estimators cannot exist. Practitioners are guaranteed to find the optimal parameter estimators through an exhaustive search in the remaining regions (i.e., focused regions). The focused regions identification method is validated through two case studies in which a model based on Newton’s law of cooling and the Michaelis–Menten model are fitted to two experimental data sets, respectively. These case studies show that the focused regions identification method can find the optimal parameter estimators and the corresponding global minimum effectively and efficiently.Closedoi:doi.org/10.1115/1.4054440Close Molkeri, Abhilash; Khatamsaz, Danial; Couperthwaite, Richard; James, Jaylen; Arróyave, Raymundo; Allaire, Douglas; Srivastava, AnkitOn the importance of microstructure information in materials design: PSP vs PP Journal Article In: Acta Materialia, vol. 223, pp. 117471, 2022.Abstract | Links | BibTeX@article{molkeri2022importance, title = {On the importance of microstructure information in materials design: PSP vs PP}, author = {Abhilash Molkeri and Danial Khatamsaz and Richard Couperthwaite and Jaylen James and Raymundo Arr\'{o}yave and Douglas Allaire and Ankit Srivastava}, doi = {10.1016/j.actamat.2021.117471}, year = {2022}, date = {2022-01-15}, urldate = {2022-01-15}, journal = {Acta Materialia}, volume = {223}, pages = {117471}, abstract = {The focus of goal-oriented materials design is to find the necessary chemistry/processing conditions to achieve the desired properties. In this setting, a material’s microstructure is either only used to carry out multiscale simulations to establish an invertible quantitative process-structure-property (PSP) relationship, or to rationalize a posteriori the underlying microstructural features responsible for the properties achieved. The materials design process itself, however, tends to be microstructure-agnostic: the microstructure only mediates the process-property (PP) connection and is\textemdashwith some exceptions such as architected materials\textemdashseldom used for the optimization itself. While the existence of PSP relationships is the central paradigm of materials science, it would seem that for materials design, one only needs to focus on PP relationships. In this work, we attempt to resolve the issue whether ‘PSP’ is a superior paradigm for materials design in cases where the microstructure itself cannot be (directly) manipulated to optimize materials’ properties. To this end, we formulate a novel microstructure-aware closed-loop multi-fidelity Bayesian optimization framework for materials design and rigorously demonstrate the importance of the microstructure information in the design process. The problem considered here involves finding the right combination of chemistry and processing parameters that maximizes a targeted mechanical property of a model dual-phase steel. Our results clearly show that an explicit incorporation of microstructure knowledge in the materials design framework significantly enhances the materials optimization process. We thus prove, in a computational setting, and for a particular representative problem where microstructure intervenes to influence properties of interest, that ‘PSP’ is superior to ‘PP’ when it comes to materials design.}, keywords = {}, pubstate = {published}, tppubtype = {article} } CloseThe focus of goal-oriented materials design is to find the necessary chemistry/processing conditions to achieve the desired properties. In this setting, a material’s microstructure is either only used to carry out multiscale simulations to establish an invertible quantitative process-structure-property (PSP) relationship, or to rationalize a posteriori the underlying microstructural features responsible for the properties achieved. The materials design process itself, however, tends to be microstructure-agnostic: the microstructure only mediates the process-property (PP) connection and is—with some exceptions such as architected materials—seldom used for the optimization itself. While the existence of PSP relationships is the central paradigm of materials science, it would seem that for materials design, one only needs to focus on PP relationships. In this work, we attempt to resolve the issue whether ‘PSP’ is a superior paradigm for materials design in cases where the microstructure itself cannot be (directly) manipulated to optimize materials’ properties. To this end, we formulate a novel microstructure-aware closed-loop multi-fidelity Bayesian optimization framework for materials design and rigorously demonstrate the importance of the microstructure information in the design process. The problem considered here involves finding the right combination of chemistry and processing parameters that maximizes a targeted mechanical property of a model dual-phase steel. Our results clearly show that an explicit incorporation of microstructure knowledge in the materials design framework significantly enhances the materials optimization process. We thus prove, in a computational setting, and for a particular representative problem where microstructure intervenes to influence properties of interest, that ‘PSP’ is superior to ‘PP’ when it comes to materials design.Closedoi:10.1016/j.actamat.2021.117471Close Khatamsaz, Danial; Molkeri, Abhilash; Couperthwaite, Richard; James, Jaylen; Arróyave, Raymundo; Srivastava, Ankit; Allaire, DouglasAdaptive active subspace-based efficient multifidelity materials design Journal Article In: Materials & Design, vol. 209, pp. 110001, 2021.Abstract | Links | BibTeX@article{khatamsaz2021adaptive, title = {Adaptive active subspace-based efficient multifidelity materials design}, author = {Danial Khatamsaz and Abhilash Molkeri and Richard Couperthwaite and Jaylen James and Raymundo Arr\'{o}yave and Ankit Srivastava and Douglas Allaire}, doi = {10.1016/j.matdes.2021.110001}, year = {2021}, date = {2021-11-01}, urldate = {2021-11-01}, journal = {Materials \& Design}, volume = {209}, pages = {110001}, abstract = {Materials design calls for an optimal exploration and exploitation of the process-structure-property (PSP) relationships to produce materials with targeted properties. Recently, we developed and deployed a closed-loop multi-information source fusion (multi-fidelity) Bayesian Optimization (BO) framework to optimize the mechanical performance of a dual-phase material by adjusting the material composition and processing parameters. While promising, BO frameworks tend to underperform as the dimensionality of the problem increases. Herein, we employ an adaptive active subspace method to efficiently handle the large dimensionality of the design space of a typical PSP-based material design problem within our multi-fidelity BO framework. Our adaptive active subspace method significantly accelerates the design process by prioritizing searches in the important regions of the high-dimensional design space. A detailed discussion of the various components and demonstration of three approaches to implementing the adaptive active subspace method within the multi-fidelity BO framework is presented.}, keywords = {}, pubstate = {published}, tppubtype = {article} } CloseMaterials design calls for an optimal exploration and exploitation of the process-structure-property (PSP) relationships to produce materials with targeted properties. Recently, we developed and deployed a closed-loop multi-information source fusion (multi-fidelity) Bayesian Optimization (BO) framework to optimize the mechanical performance of a dual-phase material by adjusting the material composition and processing parameters. While promising, BO frameworks tend to underperform as the dimensionality of the problem increases. Herein, we employ an adaptive active subspace method to efficiently handle the large dimensionality of the design space of a typical PSP-based material design problem within our multi-fidelity BO framework. Our adaptive active subspace method significantly accelerates the design process by prioritizing searches in the important regions of the high-dimensional design space. A detailed discussion of the various components and demonstration of three approaches to implementing the adaptive active subspace method within the multi-fidelity BO framework is presented.Closedoi:10.1016/j.matdes.2021.110001Close Announcements April 2022 Lab Director, Dr. Douglas Allaire, served as an expert panelist on Bayesian Optimization at the Autonomous Materials Research and Development (AMRAD) Workshop in Denver, CO. March 2022 Jaylen James successfully defended his Ph.D. Thesis: “Enhancing Performance Prediction Accuracy of High Strength Alloys via Uncertainty Quantification.” Congratulations Dr. James! January 2022 Ph.D. Candidate, Danial Khatamsaz presented our paper: “Materials Design using an Active Subspace Batch Bayesian Optimization Approach” at the AIAA SciTech Forum, Jan. 3-7, in San Diego, CA. Upcoming Events Dr. Douglas Allaire will give an invited talk at the Society of Engineering Science (SES) Conference, October 16-19, 2022 in College Station, TX.