17 citations · 19 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Learning via nonlinear conjugate gradients and depth-varying neural ODEs
George Baravdish, Gabriel Eilertsen, Rym Jaroudi +3
The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding t…
math.DS2014★ 2 cited
On fundamental unifying concepts for trajectory-based slow invariant attracting manifold computation in multiscale models of chemical kinetics
Dirk Lebiedz, Jonas Unger
Chemical kinetic models in terms of ordinary differential equations correspond to finite dimensional dissipative dynamical systems involving a multiple time scale structure. Most d…
math.DS2009★ 17 cited
A variational principle for computing slow invariant manifolds in dissipative dynamical systems
Dirk Lebiedz, Jochen Siehr, Jonas Unger
A key issue in dimension reduction of dissipative dynamical systems with spectral gaps is the identification of slow invariant manifolds. We present theoretical and numerical resul…