Experimenting and theorizing in theory formation
ISMIS '86 Proceedings of the ACM SIGART international symposium on Methodologies for intelligent systems
Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Metamodelling: for bond graphs and dynamic systems
Metamodelling: for bond graphs and dynamic systems
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Global solutions for nonlinear systems using qualitative reasoning
Annals of Mathematics and Artificial Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
| Hi-index | 0.00 |
This paper proposes a novel system to discover simultaneous time differential law equations reflecting first principles underlying objective processes. The system has the power to discover equations containing hidden state variables and/or representing chaotic dynamics without using any detailed domain knowledge. These tasks have not been addressed in any mathematical and engineering domains in spite of their essential importance. Its promising performance is demonstrated through applications to both mathematical and engineering examples.