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portfolio

publications

Dynamic Modeling of P(VDF-TrFE-CTFE)-based Soft Actuators via Echo State Networks

Published in Proceedings of the 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2022

Echo state networks for dynamic modeling of electroactive polymer soft actuators.

Recommended citation: D'Anniballe, R., Erdmann, N., Selleri, G., & Carloni, R. (2022). "Dynamic Modeling of P(VDF-TrFE-CTFE)-based Soft Actuators via Echo State Networks." Proceedings of the 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 118–124.

Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle

Published in Proceedings of the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), 2024

Probabilistic deep-learning forecasts of solar irradiance in the high-latitude Norwegian setting.

Recommended citation: Erdmann, N., Bentsen, L. Ø., Stenbro, R., Riise, H. N., Warakagoda, N., & Engelstad, P. (2024). "Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle." Proceedings of the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), pp. 1599–1606.

Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle

Published in arXiv preprint arXiv:2510.26347, 2025

Reinforcement learning for pollution detection with an autonomous underwater vehicle in sparse, nonstationary environments.

Recommended citation: Zieglmeier, S., Erdmann, N., & Warakagoda, N. D. (2025). "Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle." arXiv preprint arXiv:2510.26347.

Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks

Published in Proceedings of the 40th AAAI Conference on Artificial Intelligence, 2026

Forecasting full non-parametric predictive CDFs across multiple horizons using monotonic deep lattice networks.

Recommended citation: Erdmann, N., Bentsen, L., Stenbro, R., Riise, H. N., Warakagoda, N. D., & Engelstad, P. E. (2026). "Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks." Proceedings of the 40th AAAI Conference on Artificial Intelligence.

Deep Learning Multihorizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images

Published in IEEE Journal of Photovoltaics, 2026

A comparative evaluation of three deep-learning methods for leveraging sky images in multi-horizon solar irradiance nowcasting.

Recommended citation: Eriksen, E. W., Nygård, M. M., Erdmann, N., & Riise, H. N. (2026). "Deep Learning Multihorizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images." IEEE Journal of Photovoltaics, 16(4), 594–604.

talks

teaching

Teaching Assistant

Teaching assistant, University of Groningen, 2019

Led tutorial sessions, graded assignments and exams, and coordinated a group of other teaching assistants.

PhD Teaching Component

Lecturing, assignment design & supervision, University of Oslo, Department of Technology Systems, 2024

One of the four years of my PhD is contractually dedicated to teaching, alongside formal teaching education. Activities include holding lectures, designing assignments, and assisting with exams. This currently includes independent supervision of five Master’s thesis projects.