NEWS    Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings

Date released: March 20, 2024

  •  NEWS    Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings
  • Date:

    March 20, 2024

  • Where:

    Austin, TX

  • Description:

    Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.

    The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.

  • External Link:

    https://github.com/intelligent-environments-lab/occupant_centric_grid_interactive_buildings_course?tab=readme-ov-file

  • MERL Contact:
  • Research Areas:

    Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization

    •  Deshpande, V.M., Chakrabarty, A., Vinod, A.P., Laughman, C.R., "Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States", IEEE Control Systems Letters, DOI: 10.1109/​LCSYS.2023.3334959, November 2023.
      BibTeX TR2023-138 PDF
      • @article{Deshpande2023nov,
      • author = {Deshpande, Vedang M. and Chakrabarty, Ankush and Vinod, Abraham P. and Laughman, Christopher R.},
      • title = {{Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States}},
      • journal = {IEEE Control Systems Letters},
      • year = 2023,
      • month = nov,
      • doi = {10.1109/LCSYS.2023.3334959},
      • url = {https://www.merl.com/publications/TR2023-138}
      • }
    •  Chakrabarty, A., Wichern, G., Laughman, C.R., "Meta-Learning of Neural State-Space Models Using Data From Similar Systems", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/​j.ifacol.2023.10.1843, July 2023.
      BibTeX TR2023-087 PDF Software
      • @inproceedings{Chakrabarty2023jul,
      • author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
      • title = {{Meta-Learning of Neural State-Space Models Using Data From Similar Systems}},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2023,
      • month = jul,
      • doi = {10.1016/j.ifacol.2023.10.1843},
      • url = {https://www.merl.com/publications/TR2023-087}
      • }
    •  Salatiello, A., Wang, Y., Wichern, G., Koike-Akino, T., Yoshihiro, O., Kaneko, Y., Laughman, C.R., Chakrabarty, A., "Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?", ACM e-Energy Conference, DOI: 10.1145/​3599733.3600260, June 2023.
      BibTeX TR2023-072 PDF
      • @inproceedings{Salatiello2023jun,
      • author = {Salatiello, Alessandro and Wang, Ye and Wichern, Gordon and Koike-Akino, Toshiaki and Yoshihiro, Ohta and Kaneko, Yosuke and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {{Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?}},
      • booktitle = {ACM e-Energy Conference},
      • year = 2023,
      • month = jun,
      • doi = {10.1145/3599733.3600260},
      • url = {https://www.merl.com/publications/TR2023-072}
      • }
    •  Ngheim, T.X., Drgona, J., Jones, C., Nagy, Z., Schwan, R., Dey, B., Chakrabarty, A., Di Cairano, S., Paulson, J.A., Carron, A., Zeilinger, M., Cortez, W.S., Vrabie, D.L., "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems", American Control Conference (ACC), DOI: 10.23919/​ACC55779.2023.10155901, May 2023.
      BibTeX TR2023-052 PDF
      • @inproceedings{Ngheim2023may,
      • author = {Ngheim, Truong X. and Drgona, Jan and Jones, Colin and Nagy, Zoltan and Schwan, Roland and Dey, Biswadip and Chakrabarty, Ankush and {Di Cairano}, Stefano and Paulson, Joel A. and Carron, Andrea and Zeilinger, Melanie and Cortez, Wenceslaw S. and Vrabie, Draguna L.},
      • title = {{Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems}},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • month = may,
      • doi = {10.23919/ACC55779.2023.10155901},
      • url = {https://www.merl.com/publications/TR2023-052}
      • }
    •  Zhan, S., Wichern, G., Laughman, C.R., Chong, A., Chakrabarty, A., "Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization", Energy and Buildings, DOI: 10.1016/​j.enbuild.2022.112278, Vol. 270, pp. 112278, September 2022.
      BibTeX TR2022-072 PDF
      • @article{Zhan2023jan,
      • author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chong, Adrian and Chakrabarty, Ankush},
      • title = {{Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization}},
      • journal = {Energy and Buildings},
      • year = 2022,
      • volume = 270,
      • pages = 112278,
      • month = sep,
      • doi = {10.1016/j.enbuild.2022.112278},
      • url = {https://www.merl.com/publications/TR2022-072}
      • }