Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.
Cranmer, Shirley Ho, Peter W
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Interpretability in SciML requires mechanistic understanding rather than sparsity, and prior knowledge is often essential for interpretable scientific discovery.
Survey of automated scientific discovery covering equation discovery, symbolic regression, closed-loop systems like Adam, deep neural network roles, and autonomy levels up to level 5 with no human intervention.
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Predicting intermediate-mass black hole formation in star clusters with machine learning
Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.
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On the definition and importance of interpretability in scientific machine learning
Interpretability in SciML requires mechanistic understanding rather than sparsity, and prior knowledge is often essential for interpretable scientific discovery.
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Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
Survey of automated scientific discovery covering equation discovery, symbolic regression, closed-loop systems like Adam, deep neural network roles, and autonomy levels up to level 5 with no human intervention.