Physics-Informed Machine Learning (PIML) tutorial ACC 2023

Material for the tutorial session "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems: Opportunities and Challenges" presented at the American Control Conference (ACC) 2023.

Tutorial paper

PIML tutorial paper on arxiv
PIML tutorial paper pdf.

Cite as

@article{nghiem2023physicsinformed,
      title={Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems}, 
      author={Truong X. Nghiem and Ján Drgoňa and Colin Jones and Zoltan Nagy and Roland Schwan
      and Biswadip Dey and Ankush Chakrabarty and Stefano Di Cairano and Joel A. Paulson and Andrea Carron
      and Melanie N. Zeilinger and Wenceslao Shaw Cortez and Draguna L. Vrabie},
      year={2023},
      eprint={2306.13867},
      archivePrefix={arXiv},
      primaryClass={eess.SY}
}

Authors of the paper

• Truong X. Nghiem (NAU)
• Jan Drgona (PNNL)
• Colin Jones (EPFL)
• Zoltan Nagy (UT Austin)
• Roland Schwan (EPFL)
• Biswadip Dey (Siemens Technology)
• Ankush Chakrabarty (MERL)
• Stefano Di Cairano (MERL)
• Joel Paulson (OSU)
• Andrea Carron (ETH)
• Melanie Zeilinger (ETH)
• Wenceslao Shaw Cortez (PNNL)
• Draguna Vrabie (PNNL)

Session slides

PIML tutorial session overview slides.
Slides for Colin Jones' talk.
Slides for Ankush Chakrabarty' talk.
Slides for Jan Drgona's talk.
Slides for Loris Di Natale's talk.
Slides for Biswadip Dey's talk.

Session organizers

• Truong X. Nghiem (NAU)
• Jan Drgona (PNNL)
• Colin Jones (EPFL)
• Zoltan Nagy (UT Austin)
• Ankush Chakrabarty (MERL)

Session photos