Loris Di Natale


Empa/EPFL Ph.D. Student

Physics-inspired black-box methods for building control

Swiss Federal Laboratories for Materials Science and Technology (Empa)
Überlandstrasse 129
CH-8600 Dübendorf






I am a Ph.D. student from Ecole Polytechnique Férédale de Lausanne (EPFL) working at Empa since February 2020 and funded by the NCCR automation. I previously obtained a B.Sc. in Mathematics and a M.Sc. in Energy Management and Sustainability, both from EPFL.
My main research interests include physics-inspired (Deep) Machine Learning and Reinforcement Learning (DRL), with an application to the building control problem, to sustain the current energy transition and tackle climate change. Advanced algorithms are indeed bound to support the energy systems of the future through automation, and I want to bridge the gap between the control and Machine Learning communities to design the next generation of algorithms.
More generally, I'm always looking for potential applications of AI in projects related to sustainability to bring the power of machines to ongoing projects where it can help.


Ongoing research


Given historical building data (typically from the NEST building, at Empa), we want to build a black-box pipeline to firstly fit a black-box model of the building, then train a DRL agent to control it, and finally apply the learned control policy on the building.

Introducing physics

We typically want to use Neural Networks (NNs) at the core of the building models and DRL agents because of their great expressiveness and accuracy. However, classical NNs suffer from underspecification: they learn everything from scratch without any information on the underlying dynamics. They thus require large amounts of training data to perform well, and, in particular, we cannot guarantee that they respect the laws of physics. This might lead to wrong models (e.g. heating leading to colder temperatures) or to DRL agents that take obviously wrong decisions.
One possibility to tackle this issue is to introduce physical knowledge in the NNs, leading to physics-inspired NNs (PiNNs). We are particularly interested in one category of PiNNs where the idea is to tweak the structure of the network (such as in Convolutional NNs (CNNs) or Recurrent ones (RNNs)).



Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
W. Xu, B. Svetozarevic, L. Di Natale, P. Heer, and C.N. Jones (2023).
Manuscript submitted to Applied Energy. https://arxiv.org/pdf/2310.00758.pdf.

Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
L. Di Natale, B. Svetozarevic, P. Heer, and C.N. Jones (2023).
Applied Energy 340, 121071. https://doi.org/10.1016/j.apenergy.2023.121071.

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging simple Rules

L. Di Natale, B. Svetozarevic, P. Heer, and C.N. Jones (2022).
Manuscript accepted at CDC 2023. https://arxiv.org/abs/2211.16691.

Physically consistent Neural ODEs for Learning Multi-Physics Systems
M.Zakwan*, L. Di Natale*, B. Svetozarevic, P. Heer, and C.N. Jones, G.F. Trecate (2022).
Manuscript accepted at IFAC 2023. https://arxiv.org/abs/2211.06130.

Physically consistent neural network building models: theory and analysis
L. Di Natale, B. Svetozarevic, P. Heer, and C.N. Jones (2022).
Applied Energy 325, 119806. https://doi.org/10.1016/j.apenergy.2022.119806.

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
L. Di Natale*, Y. Lian*, E.T. Maddalena*, J. Shi, and C.N. Jones (2022).
2022 IEEE 61st Conference on Decision and Control (CDC), 1111-1117. https://doi.org/10.1109/CDC51059.2022.9992445.

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
L. Di Natale, B. Svetozarevic, P. Heer, and C.N. Jones (2022).
2022 IEEE 17th International Conference on Control & Automation (ICCA), 698-703. https://doi.org/10.1109/ICCA54724.2022.9831914.

Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
L. Di Natale, B. Svetozarevic, P. Heer, and C.N. Jones (2022).
J. Phys.: Conf. Ser. 2042 012004. https://doi.org/10.1088/1742-6596/2042/1/012004.
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments.
B. Svetozarevic, C. Baumann, S. Muntwiler, L. Di Natale, P. Heer, and M. Zeilinger (2022).
Applied Energy, 118127. https://doi.org/10.1016/j.apenergy.2021.118127.

The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland.
L. Di Natale, L. Funk, M. Rüdisüli, B. Svetozarevic, G. Pareschi, P. Heer, and G. Sansavini (2021).
Energies. 14(16):4812. https://doi.org/10.3390/en14164812.


Student projects supervision

2021 - 2022

  • Master Thesis of Simon Schnellmann on Data-driven Model Predictive Control for Building Control
    Professor: Prof. John Lygeros (ETH Zürich)

2020 - 2021

  • Master Thesis of Luca Funk on Switzerland in the Energy Transition: The Role of Hydro Power and Electric Vehicles
    Co-supervisors: Dr. Martin Rüdisüli (Empa), Dr. Bratislav Svetozarevic (Empa)
    Professor: Prof. Giovanni Sansavini (ETH Zürich)

Teaching assistant activities

2022- 2023

  • Foundations of Artificial Intelligence at EFPL, Prof. Maryam Kamgarpour.

2021- 2022

  • Model Predictive Control at EFPL, Prof. Colin Jones.

2020 - 2021

  • Model Predictive Control at EFPL, Prof. Colin Jones.


Contact me for anything: .