Pseudo-Hamiltonian neural networks for learning partial differential equations.
J. Comput. Phys., March, 2024
Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor.
CoRR, 2024
Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling.
CoRR, 2024
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators.
CoRR, 2023
Pseudo-Hamiltonian system identification.
CoRR, 2023
Port-Hamiltonian Neural Networks with State Dependent Ports.
CoRR, 2022
Data quality issues for vibration sensors: a case study in ferrosilicon production.
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Proceedings of the 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things, 2022
Linearly implicit structure-preserving schemes for Hamiltonian systems.
J. Comput. Appl. Math., 2021
Linearly Implicit Local and Global Energy-Preserving Methods for PDEs with a Cubic Hamiltonian.
SIAM J. Sci. Comput., 2020
Energy-preserving methods on Riemannian manifolds.
Math. Comput., 2020
Order theory for discrete gradient methods.
CoRR, 2020
Linearly implicit local and global energy-preserving methods for Hamiltonian PDEs.
CoRR, 2019
Dissipative Numerical Schemes on Riemannian Manifolds with Applications to Gradient Flows.
SIAM J. Sci. Comput., 2018
Adaptive energy preserving methods for partial differential equations.
Adv. Comput. Math., 2018
Shape Analysis on Lie Groups and Homogeneous Spaces.
Proceedings of the Geometric Science of Information - Third International Conference, 2017