34 citations · 35 across the 6 of their papers we have counts for
6 papers
The Vanishing Gradient Problem for Stiff Neural Differential Equations
Colby Fronk, Linda Petzold
Gradient-based optimization of neural differential equations and other parameterized dynamical systems fundamentally relies on the ability to differentiate numerical solutions with…
Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods
Colby Fronk, Linda Petzold
Stiff ordinary differential equations (ODEs) are common in many science and engineering fields, but standard neural ODE approaches struggle to accurately learn these stiff systems,…
Performance Evaluation of Single-step Explicit Exponential Integration Methods on Stiff Ordinary Differential Equations
Colby Fronk, Linda Petzold
Stiff systems of ordinary differential equations (ODEs) arise in a wide range of scientific and engineering disciplines and are traditionally solved using implicit integration meth…
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods
Colby Fronk, Linda Petzold
Stiff systems of ordinary differential equations (ODEs) are pervasive in many science and engineering fields, yet standard neural ODE approaches struggle to learn them. This limita…
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Colby Fronk, Jaewoong Yun, Prashant Singh +1
Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of man…
Interpretable Polynomial Neural Ordinary Differential Equations
Colby Fronk, Linda Petzold
Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of th…