[Crib-list] SPEAKER: David Tew (ARPA-E) | CCE Seminar | Thurs., Dec. 13, 2018 | 2:00 PM | Room 32-124 (STATA)

[Shirley Entzminger]

 		Center for Computational Engineering Seminar  (CCE)



 	HOST:  Professor Alan Edelman


DATE:           Thursday, December 13, 2018

TIME:           2:00 PM - 3:00 PM

LOCATION:       Building 32, Room 124  (STATA)

 		  32 Vassar Street, Cambridge



TITLE:           Machine-Learning Enhanced Energy-Product Design


SPEAKER:        David Tew   (ARPA-E)


ABSTRACT:


Engineering design processes are generally characterized by iterative
attempts at the solution of a well-defined market problem.  Each iteration
is frequently characterized by a hypothesis generation/conceptual design
phase where low-fidelity/reduced order models are used to refine a
high-level solution concept.  In the next phase, the winning solution
concept is then subjected to a high-fidelity (i.e. expensive) detailed
design and evaluation process that ideally culminates in the demonstration
of a successful solution to the problem.  If not, the initial hypothesis
is updated using lessons learned in the evaluation phase, and iteration
continues until the solution is obtained or the effort is abandoned.


Recognizing that high-risk and high-cost design processes are frequently
significant barriers to entry for energy-efficient products and that
emerging machine learning/artificial intelligence techniques have the
potential to lower the cost and risk of certain aspects of the
above-described energy-product design process, ARPA-E is seeking to
accelerate the application of these techniques in the engineering design
process to help engineers--

1)      to develop better & more novel product concepts,

2)      to more efficiently execute the high-fidelity optimization of
          these concepts, and

3)      to execute "inverse design" (i.e. no iteration) process
          for "simple" energy products components.

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Massachusetts Institute of Technology
Cambridge, MA