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Found 4 papers, 4 papers with code
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data
We introduce Weak-PDE-LEARN, a Partial Differential Equation (PDE) discovery algorithm that can identify non-linear PDEs from noisy, limited measurements of their solutions.
Learning the Delay Using Neural Delay Differential Equations
The intersection of machine learning and dynamical systems has generated considerable interest recently.
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data
In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements of a physical system of interest.
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
We introduce a new approach for PDE discovery that uses two Rational Neural Networks and a principled sparse regression algorithm to identify the hidden dynamics that govern a system's response.
