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Found 11 papers, 5 papers with code
Towards stable real-world equation discovery with assessing differentiating quality influence
This paper explores the critical role of differentiation approaches for data-driven differential equation discovery.
Towards true discovery of the differential equations
Differential equation discovery, a machine learning subfield, is used to develop interpretable models, particularly in nature-related applications.
Directed differential equation discovery using modified mutation and cross-over operators
The discovery of equations with knowledge of the process origin is a tempting prospect.
Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery
Evolutionary differential equation discovery proved to be a tool to obtain equations with less a priori assumptions than conventional approaches, such as sparse symbolic regression over the complete possible terms library.
On the balance between the training time and interpretability of neural ODE for time series modelling
The only interpretation that could be extracted is the eigenspace of the operator, which is an ill-posed problem for a large system.
Automated differential equation solver based on the parametric approximation optimization
Only a few "cheap and dirty" numerical methods converge on a wide class of equations without parameter tuning with the lower approximation order price.
Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models
In modern data science, it is often not enough to obtain only a data-driven model with a good prediction quality.
Multi-objective discovery of PDE systems using evolutionary approach
However, this approach restricts the application to the real cases, where, for example, the form of the external forcing is of interest.
Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea
We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods.
The data-driven physical-based equations discovery using evolutionary approach
The selected terms pass to the evolutionary algorithm, which is used to evolve the selection.
Data-driven PDE discovery with evolutionary approach
The data-driven models allow one to define the model structure in cases when a priori information is not sufficient to build other types of models.
