19 citations · 29 across the 6 of their papers we have counts for
6 papers
Equation discovery framework EPDE: Towards a better equation discovery
Mikhail Maslyaev, Alexander Hvatov
Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significan…
Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery
Mikhail Maslyaev, Alexander Hvatov
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…
Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models
Alexander Hvatov, Mikhail Maslyaev, Iana S. Polonskaya +3
In modern data science, it is often not enough to obtain only a data-driven model with a good prediction quality. On the contrary, it is more interesting to understand the properti…
Multi-objective discovery of PDE systems using evolutionary approach
Mikhail Maslyaev, Alexander Hvatov
Usually, the systems of partial differential equations (PDEs) are discovered from observational data in the single vector equation form. However, this approach restricts the applic…
The data-driven physical-based equations discovery using evolutionary approach
Alexander Hvatov, Mikhail Maslyaev
The modern machine learning methods allow one to obtain the data-driven models in various ways. However, the more complex the model is, the harder it is to interpret. In the paper,…
Data-driven PDE discovery with evolutionary approach
Michail Maslyaev, Alexander Hvatov, Anna Kalyuzhnaya
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. The possible way to obtain physi…