Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Heuristics for empirical discovery
Computational models of learning
Data-driven approaches to empirical discovery
Artificial Intelligence
Eco-logic: logic-based approaches to ecological modelling
Eco-logic: logic-based approaches to ecological modelling
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
The Utility of Knowledge in Inductive Learning
Machine Learning
ACM Transactions on Mathematical Software (TOMS)
C4.5: programs for machine learning
C4.5: programs for machine learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Inducing Process Models from Continuous Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Theory Revision in Equation Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
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
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
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In this chapter, we focus on the equation discovery task, i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain knowledge in the process of equation discovery. The proposed methodology transforms the available domain knowledge to a grammar specifying the space of candidate equation-based models. We show here how various aspects of knowledge about modeling dynamic systems in a particular domain of interest can be transformed to grammars. Thereafter, the equation discovery method Lagramgecan search through the space of models specified by the grammar and find ones that fit measured data well. We illustrate the utility of the proposed methodology on three modeling tasks from the domain of Environmental sciences. All three tasks involve establishing models of real-world systems from noisy measurement data.