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Unravelling interior evolution of terrestrial planets using Machine Learning
Siddhant Agarwal, ESO AIA2019
PhD Supervisors
Dr. Nicola Tosi
Prof. Dr. Doris Breuer
Dr. Pan Kessel
Dr. Grégoire Montavon
Prof. Dr. Klaus-Robert Müller
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 1
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Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 2
• Introduction to mantle convection and the inverse problem
• Data used for inversion
• Results using Mixture Density Networks
• Next steps using this approach
• Acknowledgements
• References
Agenda
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Introduction
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 3
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Introduction
We are interested in understanding thermal evolution of terrestrial planets like Mars and Earth.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 4
[1]
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Introduction
We are interested in understanding thermal evolution of terrestrial planets like Mars and Earth.
Mantle convection is an important driver of it.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 5
[2]
[1]
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Introduction
Over geological time scales, rocks behave likes fluids.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 6
[7]
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Introduction
Over geological time scales, rocks behave likes fluids.
Hence we use fluid dynamics simulations to study mantle convection.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 7
[7]
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Introduction
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 8
Inputs
Viscosity
Initial temperature
Radiogenic elements
⁞
Outputs
Surface heat flux
Radial contraction
Crustal thickness
⁞
In-house C++ code
Conservation of mass:𝜕𝜌
𝜕𝑡+ ∇ . 𝜌𝑢 = 0
Conservation of momentum:
𝐷𝜌𝑢
𝐷𝑡= −∇𝑃 + ∇. 𝜏 + 𝜌 Ԧ𝑔
Conservation of energy:
𝜌𝑐𝑝𝐷𝑇
𝐷𝑡− 𝛼𝑇
𝐷𝑃
𝐷𝑡= ∇. 𝑘∇𝑇 + 𝜌𝐻 + 𝜙
• Mantle convection is governed by several poorly constrained parameters and initial conditions
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Introduction
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 9
Inputs
Viscosity
Initial temperature
Radiogenic elements
⁞
Outputs
Surface heat flux
Radial contraction
Crustal thickness
⁞
• Mantle convection is governed by several poorly constrained parameters and initial conditions
• In planetary science, the outputs are observable (…sometimes)
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Introduction
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 10
Inputs
Viscosity
Initial temperature
Radiogenic elements
⁞
Outputs
Surface heat flux
Radial contraction
Crustal thickness
⁞
• Mantle convection is governed by several poorly constrained parameters and initial conditions
• In planetary science, the outputs are observable (...sometimes)
• Need Machine Learning for rapid inversion in high-dimensional spaces; Monte Carlo methods are
computationally unfeasible
[3]
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Dataset
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 11
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Dataset
• Generated some 3200 2D, quarter-cylinder evolution simulations for Mars, with:
oCompressible convection (Extended-Bousinessq Approximation)
oHeat production from core and radiogenic elements
o Temperature and pressure dependent viscosity (Arrhenius)
o Temperature and pressure dependent thermal conductivity and thermal expansion
oSolid phase transitions
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 12
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Dataset
• Generated some 3200 2D, quarter-cylinder evolution simulations for Mars, with:
oCompressible convection (Extended-Bousinessq Approximation)
oHeat production from core and radiogenic elements
o Temperature and pressure dependent viscosity (Arrhenius)
o Temperature and pressure dependent thermal conductivity and thermal expansion
oSolid phase transitions
• ~800 GB of data generated using 60,000 CPU hours; ~800 MB used for ML
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 13
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Dataset
• Generated some 3200 2D, quarter-cylinder evolution simulations for Mars, with:
oCompressible convection (Extended-Bousinessq Approximation)
oHeat production from core and radiogenic elements
o Temperature and pressure dependent viscosity (Arrhenius)
o Temperature and pressure dependent thermal conductivity and thermal expansion
oSolid phase transitions
• ~800 GB of data generated using 60,000 CPU hours; ~800 MB used for ML
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 14
Parameters
Ra (ηref ϵ [1019, 1022 Pa s])
RaQ (Λ ϵ [1, 50], ηref)
Tinitial ϵ [1600, 1800 K]
E ϵ [105, 5x105 J mol-1]
V ϵ [4x10-6, 10x10-6 m3 mol-1]
“Observables”
Temperature profile
Radial contraction
Elastic lithospheric thickness
Core heat flux
Surface heat flux
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Dataset
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 15
“Observables”
Temperature profile
Radial contraction
Elastic lithospheric thickness
Core heat flux
Surface heat flux
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 16
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Results
• Mixture Density Network (MDN) is promising for inverse problems.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 17
[5]
[4]
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Results
• Mixture Density Network (MDN) is promising for inverse problems.
• Based on the algorithm by Bishop [6]
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 18
𝐸𝑆 =1
2
𝑞=1
𝑛
𝑘=1
𝑐
𝑓𝑘 𝑥𝑞; 𝑤 − 𝑡𝑘𝑞 2
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Results
• Mixture Density Network (MDN) is promising for inverse problems.
• Based on the algorithm by Bishop [6]
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 19
𝐸𝑆 =1
2
𝑞=1
𝑛
𝑘=1
𝑐
𝑓𝑘 𝑥𝑞; 𝑤 − 𝑡𝑘𝑞 2
𝐸𝑆 = − ln
𝑖=1
𝑚
𝛼𝑖 𝑥𝑞 ∅𝑖 𝑡
𝑞 𝑥𝑞
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Results
• Mixture Density Network (MDN) is promising for inverse problems.
• Based on the algorithm by Bishop [6]
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 20
𝐸𝑆 =1
2
𝑞=1
𝑛
𝑘=1
𝑐
𝑓𝑘 𝑥𝑞; 𝑤 − 𝑡𝑘𝑞 2
𝐸𝑆 = − ln
𝑖=1
𝑚
𝛼𝑖 𝑥𝑞 ∅𝑖 𝑡
𝑞 𝑥𝑞
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 21
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 22
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 23
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 24
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 25
Using loss as a measure of ‘constrainability’
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 26
Asymptotic behavior of loss shows number of simulations is sufficient for this set of parameters and observables
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 27
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 28
σ = 0% noise σ = 1% noise σ = 10% noise
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Results
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 29
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Future steps
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 30
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Future steps
• Generate a new dataset with more parameters and observables
o Increased degeneracy and uncertainty expected
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 31
0.628
Parameters
Ra (ηref ϵ [1019, 1022 Pa s])
RaQ (Λ ϵ [1, 50], ηref)
Tinitial ϵ [1600, 1800 K]
E ϵ [105, 5x105 J mol-1]
V ϵ [4x10-6, 10x10-6 m3 mol-1]
Δ Tcmb ϵ [0, 300 K]
Kregolith, Dregolith ϵ [[const], [0, 5km]]
Observables
Temperature profile
Radial contraction
Elastic lithospheric thickness
Core heat flux
Surface heat flux
Crustal thickness
Duration of volcanism
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Future steps
• Generate a new dataset with more parameters and observables
• Investigate higher-dimensional observables
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 32
0.628
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Future steps
• Generate a new dataset with more parameters and observables
• Investigate higher-dimensional observables
• Explore some algorithmic modifications
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 33
0.628
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Future steps
• Generate a new dataset with more parameters and observables
• Investigate higher-dimensional observables
• Explore some algorithmic modifications
• Quantify precision requirements for each observable
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 34
0.628
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Future steps
• Generate a new dataset with more parameters and observables
• Investigate higher-dimensional observables
• Explore some algorithmic modifications
• Quantify precision requirements for each observable
• Build a parameter-dimensional marginal PDF [4]
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 35
0.628
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Acknowledgments
I acknowledge the North-German Supercomputing Alliance (HLRN) for providing HPC resources that have
contributed to the research results reported in this presentation.
I acknowledge the support of the Helmholtz Einstein International Berlin Research School in Data Science
(HEIBRiDS), the German Aerospace Center (DLR) and Technical University of Berlin (TUB).
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References
1. “NASA's InSight Mars Lander.” NASA, NASA, 26 June 2019, mars.nasa.gov/insight/.
2. Gonzalez, Carlos. “What's the Difference Between Conduction, Convection, and Radiation?” Machine Design, 7 Aug. 2017,
www.machinedesign.com/whats-difference-between/what-s-difference-between-conduction-convection-and-radiation.
3. Marshall, Rick. “Terminator: Dark Fate: Everything We Know about the New Movie so Far.” Www.digitaltrends.com, Digital Trends, 23
May 2019, 12:06PM PST, www.digitaltrends.com/movies/terminator-sequel-trilogy-news-cast/.
4. Atkins, Suzanne, et al. “Using Pattern Recognition to Infer Parameters Governing Mantle Convection.” Physics of the Earth and
Planetary Interiors, vol. 257, 2016, pp. 171–186., doi:10.1016/j.pepi.2016.05.016.
5. Wit, Ralph W. L. De, et al. “Bayesian Inference of Earth's Radial Seismic Structure from Body-Wave Traveltimes Using Neural Networks.”
Geophysical Journal International, vol. 195, no. 1, 2013, pp. 408–422., doi:10.1093/gji/ggt220.
6. Bishop, Christopher M. “Mixture Density Networks.” Neural Computing Research Group, Feb. 1994, doi:https://www.microsoft.com/en-
us/research/wp-content/uploads/2016/02/bishop-ncrg-94-004.pdf.
7. Tosi, Nicola, et al. “Interior Dynamics and Evolution of the Terrestrial Planets.” 5th Potsdam - Berlin Colloquium. 2019, Berlin.
Siddhant Agarwal • ESO AIA2019: Unravelling interior evolution of terrestrial planets using Machine LearningDLR.de • Chart 37