1. Satellite-Forming Impact Simulations (Past, Present, and Funded Future)‏ Brian Enke Southwest Research Institute benke@boulder.swri.edu

2. In the beginning...  Directed Learning (AI)‏  Support Vector Machines (SVMs)‏  Simulation efficiency  Increase science ROI  Continuous parameter landscape  Non-chaotic behaviour  Specific, quantifiable objectives  High dimensionality is OK  Normalized, graded output 2001: NASA Intelligent Systems project WANTED: A Few Good Sims... Magnetosphere

3.  Asteroid impact simulations (binaries)‏  Magnetosphere inversions  Asteroid impact simulations (SFDs)‏  Titan Radiative Transfer Model  Dust-lifting on Mars  Mars crater detection  SPH: Erik Asphaug  N-body: Derek Richardson  Companion: Zoe Leinhardt  AI: Mike Burl, Dennis DeCoste, Dominic Mazzoni, Lucas Scharenbroich  Grading, scheduling, integration: Brian Enke  Science: Bill Merline, Dan Durda, Bill Bottke Early Candidate Sims: Tools, Collaborators:

4.  Materials: Basalt (constant)‏  Target Diameter: 100 km (constant)‏  Impactor Velocity (3 -> 7 km/sec)‏  Impact Angle (15 -> 75 degrees)‏  Impactor Diameter ratio (1.0->3.0)‏ (46, 34, 25, 18, 14, 10 km’s)  Non-catastrophic (>50% mass in LR)  Diameter of combined SMATS / LR  No EEBs!  Threshold of 0.03 Input Parameters: Early Satellite Results (first grading formula):

5. 207 Run Grid Simulates a 17x17x21 grid (6069 pts) High res: 3-4, 30-45, 1.8-2.2 Trolling for patterns

6. 137 Active Learning runs Not bad!

7.  Resolution  Impact Angles  Noise  Preserving volume  Grading (no EEBs)  Completion and other AI details....  FUNDING Some Concerns:

9.  Emma  Karin  Baptistina Size-Frequency Distributions (no AI)

10. In the present...  Rigid N-body aggregates!  Spins!  More impacts!  … leading to….

11. Evolution of irregularly shaped binaries (SMATS or EEBs)

12. Plus... Colors!  Thermal evolution  Source (depth) of binaries

13. … And Rubble Piles!  Solid or rubble impactors  Limited to spheres/blobs if automated (anything is possible by hand)

14. … And other materials!  Basalt  Dirty Ice  Iron  Dirty Ice  SMATS  2.5 km imp.  1 km/sec Target diam (km)

15. … And other materials!  Basalt  Dirty Ice  Iron  Dirty Ice  EEBs  2.5 km imp.  1 km/sec Target diam (km)

16. … And other materials!  Basalt  Dirty Ice  Iron  Dirty Ice  SMATS  2.5 km imp.  2 km/sec Target diam (km)

17. … And other materials!  Basalt  Dirty Ice  Iron  Dirty Ice  EEBs  2.5 km imp.  2 km/sec Target diam (km)

18. In the future, All these things plus...

19. Back to AI…  2 years of funding: NASA AIS  From thresholds to peak values, continuous-valued landscapes  Resource optimization, completion, early termination  Better, variable balance of exploration vs exploitation  Better visualization of results  Higher dimensions, MCMC sampling, sim_explore…  SMATS and EEBs ??? Directed Exploration of Complex Systems