mars’ thermal evolution from machine-learning-based 1d … · 2020. 5. 7. · siddhant agarwal...
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Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
Siddhant Agarwal, May 7 2020, EGU
Nicola Tosi
Doris Breuer
Sebastian Padovan
Pan Kessel
Grégoire Montavon
HPC Resources: North-German Supercomputing Alliance (HLRN)
Funding: Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS)
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Agenda
•Motivation for using machine learning
• Dataset of Mars evolution simulations
• Training results from a neural network
• Evolutions of 1D temperature profiles using trained neural networks
• Conclusion
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
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Motivation
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Motivation
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
• Several parameters governing evolution are unknown
• Convection simulations are expensive
• Scaling laws are limited to low-dimensions
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Motivation
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
• Can a ML algorithm learn a higher-dimensional mapping?
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Dataset
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Dataset
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
Generated ~10,000 evolution simulations for Mars with:
• Compressible convection (EBA)
• Heat production from core and radiogenic elements
• Temperature and pressure dependent viscosity (Arrhenius)
• Temperature and pressure dependent thermal conductivity
and thermal expansion
• Solid phase transitions
• Melting
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Dataset
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
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Training
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Training
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
80%
10%
10%
Training
Validation
Testing
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Training
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
Some randomly selected profiles from the test set
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Evolution
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Evolution
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
The trained Neural Network can be used to generate evolutions
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Conclusion
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Conclusion
Siddhant Agarwal • EGU 2020: Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
• Higher-dimensional ML algorithms can be leveraged for surrogate modelling in
mantle convection.
• It is data-intensive, but effective.
• Future work: predict 2D temperature fields