A Bayesian experimental autonomous researcher for mechanical design-Reference-Cited by-同舟云学术

A Bayesian experimental autonomous researcher for mechanical design

Published:2020-04-10 Issue:15 Volume:6 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Gongora Aldair E.¹^ORCID,Xu Bowen¹^ORCID,Perry Wyatt¹,Okoye Chika¹,Riley Patrick²^ORCID,Reyes Kristofer G.³^ORCID,Morgan Elise F.¹⁴⁵^ORCID,Brown Keith A.¹⁵⁶^ORCID

Affiliation:

1. Department of Mechanical Engineering, Boston University, Boston, MA 02215, USA.

2. Google Research, Mountain View, CA 94043, USA.

3. Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA.

4. Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

5. Division of Materials Science and Engineering, Boston University, Boston, MA 02215, USA.

6. Physics Department, Boston University, Boston, MA 02215, USA.

Abstract

Automated testing, Bayesian optimization, and additive manufacturing combine for the autonomous design of structures.

Funder

National Science Foundation

Google

Boston University

Boston University Rafik B. Hariri Institute for Computing and Computational Science & Engineering

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference35 articles.

1. Topology optimization approaches

2. Spin-it

3. Design of manufacturable 3D extremal elastic microstructure

4. Data-driven bending elasticity design by shell thickness;Zhang X.;Eurographics Symp. Geom. Process.,2016

5. Algorithm-driven design of fracture resistant composite materials realized through additive manufacturing;Gu G. X.;Addit. Manuf.,2017

Cited by 151 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bayesian-optimization-assisted efficient operation for direct ammonia solid oxide fuel cells;Journal of Power Sources;2024-11

2. A physics-informed impact model refined by multi-fidelity transfer learning;Extreme Mechanics Letters;2024-11

3. Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward;Macromolecules;2024-09-07

4. Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys;Materials;2024-08-14

5. Self-Driving Laboratories for Chemistry and Materials Science;Chemical Reviews;2024-08-13