Loris Di Natale

Dr.

Loris Di Natale

PhD Student

Alumni

Empa, EPF Lausanne

loris.dinatale@empa.ch

Curriculum Vitae

Automation is everywhere and more and more data is available - Machine Learning can help bring both together

Loris was a PhD student on data-driven control of energy systems in the Urban Energy Systems lab at Empa. He was supervised by Prof. Colin Jones in the Automatic Lab at EPFL. He received his Bachelor's in Mathematics (2017) and his Master's in Energy Management and Sustainability (2019) from EPFL.

After completing his PhD Loris took up a position at Zurich Soft Robotics.

Scientific Publications

Published

Stable Linear Subspace Identification: A Machine Learning Approach

ArXiv

Loris Di Natale, Muhammad Zakwan, Bratislav Svetozarevic, Philipp Heer, Giancarlo Ferrari-Trecate, Colin Jones

2023

Doi.org Collection

Published

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging simple Rules

ArXiv

Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Jones

2022

Doi.org Collection Arxiv.org

Published

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

Proceedings of IFAC World congress 2023

Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Jones, Giancarlo Ferrari-Trecate

2022

Doi.org Arxiv.org

to Appear

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

Applied Energy

Vol 325

Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Jones

2022

Doi.org Collection

Published

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

IEEE 17th International Conference on Control & Automation (ICCA)

Pages 698-703

Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Jones

2022

Doi.org Collection

Published

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

Applied Energy

Vol 307 Pages 118127

Bratislav Svetozarevic, C. Baumann, S. Muntwiler, Loris Di Natale, Melanie Zeilinger, Philipp Heer

2022

Doi.org Collection

Published

Deep Reinforcement Learning for Room Temperature Control: A Black-box Pipeline from Data to Policies

CISBAT 2021 special issue of IOP's JOURNAL OF PHYSICS Conference Series

Vol 2042

Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Jones

2021

Doi.org Collection

The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland

MDPI Energies

Vol 14 No 16

Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Ciacomo Pareschi, Philipp Heer, Giovanni Sansavini

2021

Collection

News & Insights

27-03-2023

Going back to what we know: Injecting physical insights into neural networks

researcher blog

While neural networks often achieve impressive performance, they sometimes fail to capture simple concepts that humans understand intuitively – a gap that may even put lives at risk. But can we transfer our human knowledge to neural networks to help them learn more meaningful solutions? NCCR Automation researcher Loris Di Natale has a plan to make sure automated machines don’t freeze us out.

Read more about Going back to what we know: Injecting physical insights into neural networks