5 citations · 6 across the 3 of their papers we have counts for
3 papers
q-fin.ST2023★ 1 cited
Parameterized Neural Networks for Finance
Daniel Oeltz, Jan Hamaekers, Kay F. Pilz
We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specifi…
physics.chem-ph2022
Interatomic-Potential-Free, Data-Driven Molecular Dynamics
J. Bulin, J. Hamaekers, M. P. Ariza +1
We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e.g., from ab initio ca…
stat.ML2016★ 5 cited
Localized Coulomb Descriptors for the Gaussian Approximation Potential
James Barker, Johannes Bulin, Jan Hamaekers +1
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential ap…