Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Advances in genetic programming
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Genetic Programming Prediction of Stock Prices
Computational Economics
Reasoning about nonlinear system identification
Artificial Intelligence
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
Genetic Programming and Evolvable Machines
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
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
Enhancing the Plausibility of Law Equation Discovery
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Genetic Programming with Local Hill-Climbing
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Mathematical Modeling of the Influence of RKIP on the ERK Signaling Pathway
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Evolution of mathematical models of chaotic systems based on multiobjective genetic programming
Knowledge and Information Systems
Learning Chaotic Attractors by Neural Networks
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Strongly typed genetic programming
Evolutionary Computation
Machine Learning
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Dynamics of genetic programming and chaotic time series prediction
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Age-fitness pareto optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Linear Genetic Programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Inferring systems of ordinary differential equations via grammar-based immune programming
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
IEEE Transactions on Signal Processing - Part II
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
System identification-A survey
Automatica (Journal of IFAC)
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The data-driven modeling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in real-world settings, it is typically the case that one or more variables are unmeasured or "hidden." Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverse-engineer both the form and parameters of ODE models with hidden variables.