I am pleased to announce the launch of the new JMD Webinar Series, an initiative taken by the editorial board of the Journal of Mechanical Design (JMD), serving the engineering design research community. Our goal of creating this webinar series is to share the latest research and development being published in JMD and offer a platform that allows networking among the engineering design research community. All presentation videos can be accessed from the JMD companion website1 after the event.

In February 2021, we launched the first thematic webinar, focusing on “Data-driven Approaches for Engineering Design.” I would like to thank the organizers of this inaugural event, Faez Ahmed (MIT) and Journal of Mechanical Design (JMD) Associate Editors Chris Hoyle (Oregon State University), Pingfeng Wang (University of Illinois at Urbana-Champaign), and Scott Ferguson (North Carolina State University), for all of their hard work in bringing our community together for this highly successful and informative session.

I would also especially like to thank the speakers who presented their papers during this event:

Dr. Namwoo Kang presenting: Oh, S., Jung, Y., Kim, S., Lee, I., and Kang, N., Deep Generative Design: Integration of Topology Optimization and Generative Models, J. Mech. Des. 2019; 141(11): 111405.

Dr. Conrad Tucker presenting: Shu, D., Cunningham, J., Stump, G., Miller, S. W., Yukish, M. A., Simpson, T. W., and Tucker, C. S., 3D Design Using Generative Adversarial Networks and Physics-Based Validation, J. Mech. Des. July 2020; 142(7): 071701.

Dr. Soumalya Sarkar presenting: Sarkar, S., Mondal, S., Joly, M., Lynch, M. E., Bopardikar, S. D., Acharya, R., and Perdikaris, P., Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration, J. Mech. Des. 2019; 141(12): 121001.

Dr. Yan Wang presenting: Liu, D., and Wang, Y., Multi-Fidelity Physics-Constrained Neural Network and Its Application in Materials Modeling, J. Mech. Des. 2019; 141(12): 121403.

Dr. Ramin Bostanabad presenting: Bostanabad, R., Chan, Y., Wang, L., Zhu, P., and Chen, W., Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design, J. Mech. Des. 2019; 141(11): 111402.

These brief (10–15 minutes each) and engaging presentations were attended by 120 participants and were followed by Q&As and an optional 30 minute gather.town meet-up for further discussion and networking among speakers and attendees. Upcoming quarterly series are tentatively planned for Team Science, Additive Manufacturing and Topology Optimization, and Mechanism Design. Featured papers for each session will be chosen from JMD publications from the last two years based on relevance to the theme and number of downloads, with a mix of frequently downloaded papers and papers that are newly being discovered by the community.

We thank all who joined us during the inaugural webinar and invite you, your colleagues, and students, to participate in the upcoming sessions. For more information and to register for the webinars, please visit JMD’s companion website.2 I look forward to seeing you all at the next webinar!

Footnotes