will this be my last (manual) fast pitch?€¦ · will this be my last (manual) fast pitch? machine...
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Will this be my last (manual) fast pitch?Machine Learning-Enhanced Energy-Product Development
David E. Tew, Ph.D.
Program Director
March 13, 2018
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Summary
‣What?
– Lower cost energy products & services
‣Why?
– Lower research productivity
– Daunting energy & environmental challenges
‣ How?
– Develop & employ ML-enhanced product developed tools
1Ref: Bloom et al, NBER Working Paper, ’Are good ideas getting harder to find?’
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What is ML?
2
Dog Emma
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What is the magic?
3Insert Presentation Name
Method of Least Squares
Ref: Wikipedia
1795
Carl F. Gauss
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How is it relevant?
4
Example Dimensionality Reduction
Emma
Complex Simple
Encoder
Complex
Encoder
1
1
1
…
1
1
1
Simple
(Lower Dimensional
Representation)
Ref: G. E. Hinton and R. R. Salakhutdinov, Reducing Dimensionality of Data with Neural Networks
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How is it useful?
5March 19, 2018
Example Optimization in lower dimensional space (E.g. Max η subject to constraints)
Decoder
1
1
1
…
1
1
0
Evaluator
Ref: Y. Yu, T. Hur and J. Jung, Deep Learning for Topology Optimization
η
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What is in the black boxes?
6March 19, 2018 Ref: Y. Yu, T. Hur and J. Jung, Deep Learning for Topology Optimization
DecoderEncoder
1
1
1
…
1
1
1
Encoder/Decoder Convolutional Neural Networks
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What is in the black boxes?
7Ref: X. Guo, W. Li, and F. Iorio, Convolutional Neural Networks for Steady Flow Approximation
Decoder
1
1
1
…
1
1
0
Evaluator η
Option 1: ‘Traditional’ CFD, FEM, etc.
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What is in the black boxes?
8Ref: X. Guo, W. Li, and F. Iorio, Convolutional Neural Networks for Steady Flow Approximation
Decoder
1
1
1
…
1
1
0
η
Option 2: ‘Speedy’ ML Surrogate Model
Evaluator
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Design Trade
Must weigh cost of designing & training networks vs anticipated benefits
9Insert Presentation NameMarch 19, 2018
Complex Simple
Encoder
1
1
1
…
1
1
1
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Design Trade
Must weigh cost of designing & training networks vs anticipated benefits
10March 19, 2018
Simple
Encoder
Ref: Y. Yu, T. Hur and J. Jung, Deep Learning for Topology Optimization
Very Complex!
Requires
• Data
• Computing Power
• Engineering Talent
Complex
1
1
1
…
1
1
1
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Next Steps
‣ Afternoon panel discussion (3:45 – 4:45, National Harbor 10)
‣ Summit feedback (or [email protected])
‣ Potential workshop
‣ Begin (manually) preparing next year’s fast pitch