Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Reasoning about nonlinear system identification
Artificial Intelligence
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Computational Revision of Quantitative Scientific Models
DS '01 Proceedings of the 4th International Conference on Discovery Science
Theory Revision in Equation Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
Discovering admissible models of complex systems based on scale-types and identity constraints
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Effective vaccination policies
Information Sciences: an International Journal
Toward robust real-world inference: a new perspective on explanation-based learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Developing mathematical models that represent physical devices is a difficult and time consuming task. In this paper, we present a hybrid approach to modeling that combines machine learning methods with knowledge from a human domain expert. Specifically, we propose a system for automatically revising an initial model provided by an expert with an equation discovery program that is tightly constrained by domain knowledge. We apply our system to learning an improved model of a battery on the International Space Station from telemetry data. Our results suggest that this hybrid approach can reduce model development time and improve model quality.