2 citations · 2 across the 4 of their papers we have counts for
4 papers
Learning Deterministic and Stochastic Forced Hamiltonian Systems
Benedikt Brantner, Tomasz Tyranowski
We develop a geometric framework for learning deterministic and stochastic forced Hamiltonian systems with neural networks. Motivated by the Lagrange-d'Alembert principle and the t…
Symplectic Autoencoders for Model Reduction of Hamiltonian Systems
Benedikt Brantner, Michael Kraus
Many applications, such as optimization, uncertainty quantification and inverse problems, require repeatedly performing simulations of large-dimensional physical systems for differ…
Volume-Preserving Transformers for Learning Time Series Data with Structure
Benedikt Brantner, Guillaume de Romemont, Michael Kraus +1
Two of the many trends in neural network research of the past few years have been (i) the learning of dynamical systems, especially with recurrent neural networks such as long shor…
Generalizing Adam to Manifolds for Efficiently Training Transformers
Benedikt Brantner
One of the primary reasons behind the success of neural networks has been the emergence of an array of new, highly-successful optimizers, perhaps most importantly the Adam optimize…