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Realistic On-the-fly Outcomes of Planetary Collisions: Machine Learning Applied to Simulations of Giant Impacts

  • Authors: Saverio Cambioni, Erik Asphaug, Alexandre Emsenhuber, Travis S. J. Gabriel, Roberto Furfaro, and Stephen R. Schwartz

2019 The Astrophysical Journal 875 40.

  • Provider: AAS Journals

Caption: Figure 5.

Left-hand panel: evolution of the Mean Square Error (MSE) for training, testing and validation, for increasing epochs of training. When validation is concluded, the average plateau value of the testing MSE is 0.04. This quantifies the global uncertainty of the surrogate model in mimicking the parent numerical model; i.e., the SPH simulations. Right-hand panel: correlation between predictions and target, and overall fitting with respect to an expected 1:1 line. The regression index R is about 96% (average), close to the optimal value of 100%.

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