Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data
Authors:
- 8 July 2024
- journal article
- research article
- Published by Elsevier in Journal of Computational Physics
- Vol. 514
Abstract
No abstract availableKeywords
This publication has 46 references indexed in Scilit:
- Learning sparse nonlinear dynamics via mixed-integer optimizationNonlinear Dynamics, 2023
- Sparse dynamical system identification with simultaneous structural parameters and initial condition estimationChaos, Solitons & Fractals, 2022
- Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressionsChemical Engineering Journal, 2022
- Overview of Surrogate Modeling in Chemical Process EngineeringChemie Ingenieur Technik, 2019
- CasADi: a software framework for nonlinear optimization and optimal controlMathematical Programming Computation, 2018
- Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operationsComputers & Chemical Engineering, 2018
- A global MINLP approach to symbolic regressionMathematical Programming, 2018
- A hybrid evolutionary algorithm for the symbolic modeling of multiple-time-scale dynamical systemsEvolutionary Intelligence, 2015
- Inference of hidden variables in systems of differential equations with genetic programmingGenetic Programming and Evolvable Machines, 2012
- Genetic programming as a means for programming computers by natural selectionStatistics and Computing, 1994