Symbolic Learning and Artificial Intelligence
Symbolic learning, the classical artificial intelligence, is a set of methods for learning symbolic equations from data and numerical functions. SciML offers an array of symbolic learning utilities which connect with the other machine learning and equation solver functionalities to make it easy to embed prior knowledge and discover missing physics. For more information, see Universal Differential Equations for Scientific Machine Learning.
DataDrivenDiffEq.jl: Data-Driven Modeling and Automated Discovery of Dynamical Systems
DataDrivenDiffEq.jl is a general interface for data-driven modeling, containing a large array of techniques such as:
- Koopman operator methods (Dynamic-Mode Decomposition (DMD) and variations)
- Sparse Identification of Dynamical Systems (SINDy and variations like iSINDy)
- Sparse regression methods (STSLQ, SR3, etc.)
- PDEFind
- Wrappers for SymbolicRegression.jl
- AI Feynman
- OccamNet
SymbolicNumericIntegration.jl: Symbolic Integration via Numerical Methods
SymbolicNumericIntegration.jl is a package computing the solution to symbolic integration problem using numerical methods (numerical integration mixed with sparse regression).
Third-Party Libraries to Note
SymbolicRegression.jl
SymbolicRegression.jl is a symbolic regression library which uses genetic algorithms with parallelization to achieve fast and robust symbolic learning.