Loris Di Natale

Physically Consistent Neural Networks for building thermal modeling: theory and analysis pdf

Bratislav Svetozarevic, Philipp Heer, Colin N. Jones, 2021, ArXiv.

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

Loris Di Natale, C. Jones, Yingzhao Lian, 2022, 2022 IEEE 61st Conference on Decision and Control (CDC).

Physically Consistent Neural Networks for building thermal modeling: theory and analysis

Loris Di Natale, C. Jones, B. Svetozarevic, 2021, Applied Energy.

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

Loris Di Natale, Colin N. Jones, G. Ferrari-Trecate, 2022, ArXiv.

Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models

Loris Di Natale, B. Svetozarevic, Philipp Heer, 2022, 2212.12380.

Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models

Loris Di Natale, B. Svetozarevic, Philipp Heer, 2022, Applied Energy.

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

Loris Di Natale, C. Jones, B. Svetozarevic, 2022, 2022 IEEE 17th International Conference on Control & Automation (ICCA).