3 papers
cs.LG2026
Symplectic convolutional neural networks
Süleyman Yıldız, Konrad Janik, Peter Benner
We propose a new symplectic convolutional neural network (CNN) architecture by leveraging symplectic neural networks, proper symplectic decomposition, and tensor techniques. Specif…
cs.LG2025
Time-adaptive HénonNets for separable Hamiltonian systems
Konrad Janik, Peter Benner
Measurement data is often sampled irregularly, i.e., not on equidistant time grids. This is also true for Hamiltonian systems. However, existing machine learning methods, which lea…
cs.LG2025
Time-adaptive SympNets for separable Hamiltonian systems
Konrad Janik, Peter Benner
Measurement data is often sampled irregularly i.e. not on equidistant time grids. This is also true for Hamiltonian systems. However, existing machine learning methods, which learn…