... by leveraging insights gained from the first vehicle type through transfer learning, coupled with data augmentation techniques. The proposed method is demonstrated by a real-world case study with a small-size SUV and a medium-size SUV. Results show substantial enhancement in the predictive performance...

... scheme. In the second scenario, we propose a time-domain method (deceleration versus time) based on temporal convolutional network (TCN) and transfer learning. An initial TCN model is first trained by fusing CAE data with physical test data of the first vehicle type based on data augmentation. This data...

..., this article explores transfer learning within the context of engineering design and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads, and applications, resulting in more flexible and data-efficient surrogate models for trusses. Email: ewhalen@mit.edu...

... limitations of learning robots in industrial settings, associated with sample inefficiency and specialization on one task through modularization and transfer learning. We evaluated the performance of the M-DDPG agent against two metrics: success rate and time-to-completion. Although we expect these two...

... [ 21 ] (5) Deep belief network-based methods [ 22 ] (6) Spectral density function-based methods [ 15 ], and (7) Transfer learning-based methods [ 23 , 24 ] Fig. 2 The architecture of the proposed GAN Figure 2 is a simple demonstration of the proposed architecture...