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20172026
most citedVariational Learning of Euler-Lagrange Dynamics from Data

15 citations · 63 across the 15 of their papers we have counts for

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9 papers · 1 filter

math.NA2024

Adaptive higher order reversible integrators for memory efficient deep learning

Sofya Maslovskaya, Sina Ober-Blöbaum, Christian Offen +2

The depth of networks plays a crucial role in the effectiveness of deep learning. However, the memory requirement for backpropagation scales linearly with the number of layers, whi…

math.NA2024

Commutator-free Cayley methods

Boris Wembe, Christian Offen, Sofya Maslovskaya +2

Differential equations posed on quadratic matrix Lie groups arise in the context of classical mechanics and quantum dynamical systems. Lie group numerical integrators preserve the…

math.NA2024

Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification

Christian Offen

We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-ba…

math.NA2024★ 2 cited

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification

Christian Offen

The article introduces a method to learn dynamical systems that are governed by Euler--Lagrange equations from data. The method is based on Gaussian process regression and identifi…

math.NA2023★ 4 cited

Learning of discrete models of variational PDEs from data

Christian Offen, Sina Ober-Blöbaum

We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density s…

math.NA2022★ 4 cited

Backward error analysis for conjugate symplectic methods

Robert I McLachlan, Christian Offen

The numerical solution of an ordinary differential equation can be interpreted as the exact solution of a nearby modified equation. Investigating the behaviour of numerical solutio…

math.NA2021★ 15 cited

Variational Learning of Euler-Lagrange Dynamics from Data

Sina Ober-Blöbaum, Christian Offen

The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exh…

math.NA2019★ 3 cited

Detection of high codimensional bifurcations in variational PDEs

Lisa Maria Kreusser, Robert I McLachlan, Christian Offen

We derive bifurcation test equations for A-series singularities of nonlinear functionals and, based on these equations, we propose a numerical method for detecting high codimension…

math.NA2018★ 2 cited

Symplectic integration of PDEs using Clebsch variables

Robert I McLachlan, Christian Offen, Benjamin K Tapley

Many PDEs (Burgers' equation, KdV, Camassa-Holm, Euler's fluid equations,...) can be formulated as infinite-dimensional Lie-Poisson systems. These are Hamiltonian systems on manifo…