Topics: Digital twin , Manufacturing , Optimization , Physics , Process control , Process design , Uncertainty , Additive manufacturing , Lasers , Porosity

Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Yonatan Ashenafi, Piyush Pandita, Sayan Ghosh

J. Mech. Des. September 2022, 144(9): 091705. doi: https://doi.org/10.1115/1.4054631

Abstract

View articletitled, Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

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Topics: Algorithms , Design , Experimental design , Optimization , Airfoils , Reinforcement learning

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Lichuan Ren, Zhimin Xi

J. Mech. Des. September 2022, 144(9): 091706. doi: https://doi.org/10.1115/1.4054674

Abstract

View articletitled, Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

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Topics: Autonomous vehicles , Control equipment , Design , Dynamic models , Errors , Tires , Uncertainty , Vehicles , Simulation , Roads

Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

Yu Ao, Yunbo Li, Jiaye Gong, Shaofan Li

J. Mech. Des. September 2022, 144(9): 091707. doi: https://doi.org/10.1115/1.4053816

Abstract

View articletitled, Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

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Topics: Algorithms , Artificial neural networks , Design , Geometry , Hull , Ships , Artificial intelligence

Design of Unmanned Cable Shovel Based on Multiobjective Co-Design Optimization of Structural and Control Parameters

Tianci Zhang, Tao Fu, Xueguan Song

J. Mech. Des. September 2022, 144(9): 091708. doi: https://doi.org/10.1115/1.4054808

Abstract

View articletitled, Design of Unmanned Cable Shovel Based on Multiobjective Co-Design Optimization of Structural and Control Parameters

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Topics: Cables , Design , Optimization , Trajectories (Physics)

Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models

Yixuan Liu, Dakota Barthlow, Zissimos P. Mourelatos, Jice Zeng, David Gorsich, Amandeep Singh, Zhen Hu

J. Mech. Des. September 2022, 144(9): 091709. doi: https://doi.org/10.1115/1.4054908

Abstract

View articletitled, Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models

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Topics: Mechanical admittance , Roads , Simulation , Vehicles , Automated guided vehicles

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

Michael G. Kapteyn, Karen E. Willcox

J. Mech. Des. September 2022, 144(9): 091710. doi: https://doi.org/10.1115/1.4054907

Abstract

View articletitled, Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

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Topics: Digital twin , Sensors , Unmanned aerial vehicles , Design

Dynamic Resource Allocation in Systems-of-Systems Using a Heuristic-Based Interpretable Deep Reinforcement Learning

Qiliang Chen, Babak Heydari

J. Mech. Des. September 2022, 144(9): 091711. doi: https://doi.org/10.1115/1.4055057

Abstract

View articletitled, Dynamic Resource Allocation in Systems-of-Systems Using a Heuristic-Based Interpretable Deep Reinforcement Learning

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Topics: Reinforcement learning , Resource allocation , Silicon-on-sapphire , Algorithms

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Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

Design of Unmanned Cable Shovel Based on Multiobjective Co-Design Optimization of Structural and Control Parameters

Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

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