3 citations · 4 across the 4 of their papers we have counts for
4 papers
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data
Robert Stephany, Christopher Earls
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. Weak-P…
Learning the Delay Using Neural Delay Differential Equations
Maria Oprea, Mark Walth, Robert Stephany +3
The intersection of machine learning and dynamical systems has generated considerable interest recently. Neural Ordinary Differential Equations (NODEs) represent a rich overlap bet…
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data
Robert Stephany, Christopher Earls
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 o…
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
Robert Stephany, Christopher Earls
PDE discovery shows promise for uncovering predictive models of complex physical systems but has difficulty when measurements are sparse and noisy. We introduce a new approach for…