92 citations · 126 across the 4 of their papers we have counts for
4 papers
Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations
Anthony G. Chesebro, David Hofmann, Vaibhav Dixit +6
Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experimental data. We present PEM-UDE,…
Bayesian Neural Ordinary Differential Equations
Raj Dandekar, Karen Chung, Vaibhav Dixit +4
Recently, Neural Ordinary Differential Equations has emerged as a powerful framework for modeling physical simulations without explicitly defining the ODEs governing the system, bu…
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
Chris Rackauckas, Mike Innes, Yingbo Ma +3
DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the…
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions
Yingbo Ma, Vaibhav Dixit, Mike Innes +2
Derivatives of differential equation solutions are commonly for parameter estimation, fitting neural differential equations, and as model diagnostics. However, with a litany of cho…