Machine Learning in Differential Equations

Dear Colleagues,

The aim of this Special Issue is to present recent advances and new machine-learning methods to model and solve differential equations. Topics include but are not limited to:

• Genetic programming and differential equations;
• Grammatical evolution and differential equations;
• Inference of differential equation models;
• Estimation of chaotic ordinary differential equations;
• Time-series forecasting using differential equations;
• Inverse problems and machine learning models;
• Parallel and distributed techniques to solve differential equations;
• Numerical integration in neural ordinary differential equations;
• Recurrent neural networks applied to differential equations;
• Machine learning for continuous-time finance;
• Artificial neural networks for solving fuzzy differential equations;
• Stochastic differential equations and machine learning methods;
• Machine learning of space-fractional differential equations;
• Software specialized to solve differential equations;
• Using differential evolution based methods to solve differential equations;
• Automatic differentiation and differential equations;
• Interval algorithms and differential equations.

Dr. Ioannis G. Tsoulos
Guest Editor

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