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1. Additive manufacturing as a tool for high-throughput experimentation

   https://doi.org/10.20517/jmi.2022.19  

   Xiong, Wei (January 2022, Journal of Materials Informatics)

   Additive manufacturing (AM) is a disruptive technology with a unique capability in fabricating parts with complex geometry and fixing broken supply chains. However, many AM techniques are complicated with their processing features due to complex heating and cooling cycles with the melting of feedstock materials. Therefore, it is quite challenging to directly apply the materials design and processing optimization method used for conventional manufacturing to AM techniques. In this viewpoint paper, we discuss some of the ongoing efforts of high-throughput (HT) experimentation, which can be used for materials development and processing design. Particularly, we focus on the beam- and powder-based AM techniques since these methods have demonstrated success in HT experimentation. In addition, we propose new opportunities to apply AM techniques as the materials informatic tools contributing to materials genome. 

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2. Iterative Maneuver Optimization in a Transverse Gust Encounter

   https://doi.org/10.2514/1.J062404  

   Xu, Xianzhang; Gementzopoulos, Antonios; Sedky, Girguis; Jones, Anya R.; Lagor, Francis D. (May 2023, AIAA Journal)

   This paper presents a framework based on either iterative simulation or iterative experimentation for constructing an optimal, open-loop maneuver to regulate the aerodynamic force on a wing in the presence of a known flow disturbance. The authors refer to the method as iterative maneuver optimization and apply it in this paper to regulate lift on a pitching wing during a transverse gust encounter. A candidate maneuver is created by performing an optimal control calculation on a surrogate model of the wing–gust interaction. Execution of the proposed maneuver in a high-fidelity simulation or experiment provides an error signal based on the difference between the force predicted by the surrogate model and the measured force. The error signal provides an update to the reference signal used by the surrogate model for tracking. A new candidate maneuver is calculated such that the surrogate model tracks the reference force signal, and the process repeats until the maneuver adequately regulates the force. The framework for iterative maneuver optimization is tested on a discrete vortex model as well as in experiments in a water towing tank. Experimental results show that the proposed framework generates a maneuver that reduces the magnitude of lift overshoot by 92% for a trapezoidal gust with peak velocity equal to approximately 0.7 times the freestream flow speed. 

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3. Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization

   Gong, Chengyue; Peng, Jian; Liu, Qiang (January 2019, international conference on machine learning)

   Batch Bayesian optimization has been shown to be an efficient and successful approach for black-box function optimization, especially when the evaluation of cost function is highly expensive but can be efficiently parallelized. In this paper, we introduce a novel variational framework for batch query optimization, based on the argument that the query batch should be selected to have both high diversity and good worst case performance. This motivates us to introduce a variational objective that combines a quantile-based risk measure (for worst case performance) and entropy regularization (for enforcing diversity). We derive a gradient-based particle-based algorithm for solving our quantile-based variational objective, which generalizes Stein variational gradient descent (SVGD). We evaluate our method on a number of real-world applications and show that it consistently outperforms other recent state-of-the-art batch Bayesian optimization methods. Extensive experimental results indicate that our method achieves better or comparable performance, compared to the existing methods. 

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4. Performance-driven Wire Sizing for Analog Integrated Circuits

   Li, Y.; Lin, Y.; Madhusudan, M.; Sharma, A.; Sapatnekar, S.; Harjani, R.; Hu, J (January 2022, ACM transactions on design automation of electronic systems)

   Analog IC performance has a strong dependence on interconnect RC parasitics, which are significantly affected by wire sizes in recent technologies, where minimum-width wires have high resistance. However, performance-driven wire sizing for analog ICs has received very little research attention. In order to fill this void, we develop several techniques to facilitate an end-to-end automatic wire sizing approach. They include a circuit performance model based on customized graph neural network (GNN) and two optimization techniques: one using Bayesian optimization accelerated by the GNN model, and the other based on TensorFlow training. Experimental results show that our technique can achieve 11% circuit performance improvement or 8.7× speedup compared to a conventional Bayesian optimization method. 

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5. Rapid Development of Metal Additive Manufacturing Using Artificial Intelligence/Machine Learning and High-Throughput Material Testing

   https://doi.org/10.1146/annurev-matsci-080423-121436  

   Adapa, Venkata_Surya Karthik; Kalidindi, Surya R; Saldana, Christopher J (July 2025, Annual Review of Materials Research)

   Metal additive manufacturing (AM) holds immense potential for developing advanced structural alloys. However, the complex, heterogeneous nature of AM-produced materials presents significant challenges to traditional material characterization and optimization methods. This review explores the integration of artificial intelligence (AI) and machine learning (ML) with high-throughput material characterization protocols to rapidly establish the process–structure–property (PSP) relationships critically needed to dramatically accelerate the development of metal AM processes. Combinatorial high-throughput evaluations, including rapid material synthesis and nonstandard high-throughput testing protocols, such as spherical indentation and small punch tests, are discussed for their capability to rapidly assess mechanical properties and establish PSP linkages. Furthermore, the review examines the role of AI and ML in optimizing AM processes, particularly through Bayesian optimization, which offers new avenues for efficient exploration of high-dimensional design spaces. The review envisions a future where AI- and ML-driven, autonomous AM development cycles significantly enhance material and process optimization. 

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