Will this be my last (manual) fast pitch?Machine Learning-Enhanced Energy-Product Development

David E. Tew, Ph.D.

Program Director

March 13, 2018

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?’

What is ML?

2

Dog Emma

What is the magic?

3Insert Presentation Name

Method of Least Squares

Ref: Wikipedia

1795

Carl F. Gauss

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

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

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

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.

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

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

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

Next Steps

‣ Afternoon panel discussion (3:45 – 4:45, National Harbor 10)

‣ Summit feedback (or david.tew@hq.doe.gov)

‣ Potential workshop

‣ Begin (manually) preparing next year’s fast pitch