Title:
AI Driven Design Approach
AI Driven Design Approach
| dc.contributor.author | Srivastava, Sanjeev | |
| dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
| dc.contributor.corporatename | Siemens Research | en_US |
| dc.date.accessioned | 2019-04-16T15:11:24Z | |
| dc.date.available | 2019-04-16T15:11:24Z | |
| dc.date.issued | 2019-04-03 | |
| dc.description | Presented on April 3, 2019 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116. | en_US |
| dc.description | Dr. Sanjeev Srivastava currently works as a Senior Key Expert Scientist at Siemens Corporation, Corporate Technology, Princeton, NJ. His research interests and expertise includes machine learning and optimization techniques for various complex engineering problems; knowledge representation; modeling and simulation of engineering systems; advanced manufacturing; power and energy management and distributed controls. | en_US |
| dc.description | Runtime: 57:51 minutes | en_US |
| dc.description.abstract | Design Space Exploration (DSE) is an activity that is performed to systematically analyze several design points and then select the design(s) based on parameters of interest and design requirements. For complex systems, design engineers spend a lot of time generating new design iterations using simulation-based tools. However, they do not explore the complete design space due to time-consuming design creation and analyses process, and hence do not generate optimal design but rather settle for a sub-optimal design that meets the requirements. Recently, there have been advances in the areas of Artificial Intelligence (AI), cognitive computing, Internet of Things, 3D printing, advanced robotics, that provide capabilities to transform the current design process by automating various design steps, speeding up the design and analysis toolchain, and creative decision making. Within Siemens CT we have been developing a Generative Design framework, which utilizes the AI and knowledge representation techniques in combination with traditional design methods to generate better designs in a faster manner. The talk will address various technical areas of this framework and the various AI methods which we are currently applying to optimally design parts, systems, and processes. | en_US |
| dc.format.extent | 57:51 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/60978 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
| dc.subject | Artificial intelligence (AI) | en_US |
| dc.subject | Design space exploration | en_US |
| dc.subject | Generative design | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | AI Driven Design Approach | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Machine Learning Center | |
| local.contributor.corporatename | College of Computing | |
| local.relation.ispartofseries | ML@GT Seminar Series | |
| relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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