Publications

70 / 3,772 publications found.


  •  Chakrabarty, A., Wichern, G., Deshpande, V.M., Vinod, A.P., Berntorp, K., Laughman, C.R., "Meta-Learning for Physically-Constrained Neural System Identification", arXiv, January 2025.
    BibTeX arXiv
    • @article{Chakrabarty2025jan,
    • author = {Chakrabarty, Ankush and Wichern, Gordon and Deshpande, Vedang M. and Vinod, Abraham P. and Berntorp, Karl and Laughman, Christopher R.}},
    • title = {Meta-Learning for Physically-Constrained Neural System Identification},
    • journal = {arXiv},
    • year = 2025,
    • month = jan,
    • url = {https://arxiv.org/abs/2501.06167v1}
    • }
  •  Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Conference on Decision and Control (CDC), December 2024.
    BibTeX TR2024-180 PDF
    • @inproceedings{Chakrabarty2024dec,
    • author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
    • title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
    • booktitle = {IEEE Conference on Decision and Control (CDC)},
    • year = 2024,
    • month = dec,
    • url = {https://www.merl.com/publications/TR2024-180}
    • }
  •  Park, Y.-J., Germain, F.G., Liu, J., Wang, Y., Koike-Akino, T., Wichern, G., Christopher R., , Azizan, N., Laughman, C.A., "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
    BibTeX TR2025-001 PDF
    • @inproceedings{Park2024dec,
    • author = {Park, Young-Jin and Germain, François G and Liu, Jing and Wang, Ye and Koike-Akino, Toshiaki and Wichern, Gordon and Christopher R. and Azizan, Navid and Laughman, Chakrabarty, Ankush}},
    • title = {Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?},
    • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    • year = 2024,
    • month = dec,
    • url = {https://www.merl.com/publications/TR2025-001}
    • }
  •  Tang, W.-T., Chakrabarty, A., Paulson, J.A., "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
    BibTeX TR2024-167 PDF
    • @inproceedings{Tang2024dec,
    • author = {Tang, Wei-Ting and Chakrabarty, Ankush and Paulson, Joel A.}},
    • title = {TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions},
    • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    • year = 2024,
    • month = dec,
    • url = {https://www.merl.com/publications/TR2024-167}
    • }
  •  Ip, J.H.S., Chakrabarty, A., Masui Hideyuki, , Mesbah, A., Romeres, D., "Preference-based Multi-Objective Bayesian Optimization with Gradients", NeurIPS Workshop on Bayesian Decision-making and Uncertainty, December 2024.
    BibTeX TR2025-011 PDF
    • @inproceedings{Ip2024dec,
    • author = {Ip, Joshua Hang Sai and Chakrabarty, Ankush and Masui Hideyuki and Mesbah, Ali and Romeres, Diego}},
    • title = {Preference-based Multi-Objective Bayesian Optimization with Gradients},
    • booktitle = {NeurIPS Workshop on Bayesian Decision-making and Uncertainty},
    • year = 2024,
    • month = dec,
    • url = {https://www.merl.com/publications/TR2025-011}
    • }
  •  Vanfretti, L., Laughman, C.R., Chakrabarty, A., "Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation", American Modelica Conference, October 2024.
    BibTeX TR2024-140 PDF
    • @inproceedings{Vanfretti2024oct,
    • author = {Vanfretti, Luigi and Laughman, Christopher R. and Chakrabarty, Ankush}},
    • title = {Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation},
    • booktitle = {American Modelica Conference},
    • year = 2024,
    • month = oct,
    • url = {https://www.merl.com/publications/TR2024-140}
    • }
  •  Yan, J., Chakrabarty, A., Rupenyan, A., Lygeros, J., "MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models", International Conference on Automation Science and Engineering (CASE), DOI: 10.1109/​CASE59546.2024.10711717, August 2024.
    BibTeX TR2024-115 PDF
    • @inproceedings{Yan2024aug,
    • author = {Yan, Jiaqi and Chakrabarty, Ankush and Rupenyan, Alisa and Lygeros, John}},
    • title = {MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models},
    • booktitle = {International Conference on Automation Science and Engineering (CASE)},
    • year = 2024,
    • month = aug,
    • doi = {10.1109/CASE59546.2024.10711717},
    • url = {https://www.merl.com/publications/TR2024-115}
    • }
  •  Chakrabarty, A., Vanfretti, L., Bortoff, S.A., Deshpande, V.M., Wang, Y., Paulson, J.A., Zhan, S., Laughman, C.R., "Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks", IEEE Conference on Control Technology and Applications (CCTA) 2024, DOI: 10.1109/​CCTA60707.2024.10666585, August 2024.
    BibTeX TR2024-113 PDF
    • @inproceedings{Chakrabarty2024aug,
    • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Bortoff, Scott A. and Deshpande, Vedang M. and Wang, Ye and Paulson, Joel A. and Zhan, Sicheng and Laughman, Christopher R.}},
    • title = {Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks},
    • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
    • year = 2024,
    • month = aug,
    • doi = {10.1109/CCTA60707.2024.10666585},
    • url = {https://www.merl.com/publications/TR2024-113}
    • }
  •  Sorouifar, F., Paulson, J.A., Wang, Y., Quirynen, R., Laughman, C.R., Chakrabarty, A., "Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems", IEEE Conference on Control Technology and Applications (CCTA), DOI: 10.1109/​CCTA60707.2024.10666537, August 2024.
    BibTeX TR2024-110 PDF
    • @inproceedings{Sorouifar2024aug,
    • author = {Sorouifar, Farshud and Paulson, Joel A. and Wang, Ye and Quirynen, Rien and Laughman, Christopher R. and Chakrabarty, Ankush}},
    • title = {Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems},
    • booktitle = {IEEE Conference on Control Technology and Applications (CCTA)},
    • year = 2024,
    • month = aug,
    • doi = {10.1109/CCTA60707.2024.10666537},
    • url = {https://www.merl.com/publications/TR2024-110}
    • }
  •  Safaoui, S., Vinod, A.P., Chakrabarty, A., Quirynen, R., Yoshikawa, N., Di Cairano, S., "Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning", IEEE Transactions on Robotics, DOI: 10.1109/​TRO.2024.3387010, Vol. 40, pp. 2529-2542, July 2024.
    BibTeX TR2024-048 PDF Video
    • @article{Safaoui2024jul,
    • author = {Safaoui, Sleiman and Vinod, Abraham P. and Chakrabarty, Ankush and Quirynen, Rien and Yoshikawa, Nobuyuki and Di Cairano, Stefano},
    • title = {Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning},
    • journal = {IEEE Transactions on Robotics},
    • year = 2024,
    • volume = 40,
    • pages = {2529--2542},
    • month = jul,
    • doi = {10.1109/TRO.2024.3387010},
    • url = {https://www.merl.com/publications/TR2024-048}
    • }
  •  Laughman, C.R., Deshpande, V.M., Chakrabarty, A., Qiao, H., "Enhancing Thermodynamic Data Quality for Refrigerant Mixtures: Domain-Informed Anomaly Detection and Removal", nternational Refrigeration and Air Conditioning Conference at Purdue, July 2024.
    BibTeX TR2024-099 PDF
    • @inproceedings{Laughman2024jul,
    • author = {Laughman, Christopher R. and Deshpande, Vedang M. and Chakrabarty, Ankush and Qiao, Hongtao}},
    • title = {Enhancing Thermodynamic Data Quality for Refrigerant Mixtures: Domain-Informed Anomaly Detection and Removal},
    • booktitle = {nternational Refrigeration and Air Conditioning Conference at Purdue},
    • year = 2024,
    • month = jul,
    • url = {https://www.merl.com/publications/TR2024-099}
    • }
  •  Vinod, A.P., Yamazaki, S., Chakrabarty, A., Yoshikawa, N., Di Cairano, S., "Aircraft Approach Management using Reachability and Dynamic Programming", American Control Conference (ACC), DOI: 10.23919/​ACC60939.2024.10644902, June 2024, pp. 318-324.
    BibTeX TR2024-079 PDF
    • @inproceedings{Vinod2024jun,
    • author = {{Vinod, Abraham P. and Yamazaki, Sachiyo and Chakrabarty, Ankush and Yoshikawa, Nobuyuki and Di Cairano, Stefano}},
    • title = {Aircraft Approach Management using Reachability and Dynamic Programming},
    • booktitle = {American Control Conference (ACC)},
    • year = 2024,
    • pages = {318--324},
    • month = jun,
    • publisher = {IEEE},
    • doi = {10.23919/ACC60939.2024.10644902},
    • url = {https://www.merl.com/publications/TR2024-079}
    • }
  •  Xu, W., Jones, C., Svetozarevic, B., Laughman, C.R., Chakrabarty, A., "Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints", Journal of Process Control, DOI: 10.1016/​j.jprocont.2024.103212, Vol. 138, April 2024.
    BibTeX TR2024-046 PDF
    • @article{Xu2024apr,
    • author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
    • title = {Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints},
    • journal = {Journal of Process Control},
    • year = 2024,
    • volume = 138,
    • month = apr,
    • doi = {10.1016/j.jprocont.2024.103212},
    • url = {https://www.merl.com/publications/TR2024-046}
    • }
  •  Yan, J., Chakrabarty, A., Rupenyan, A., Lygeros, J., "MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models", arXiv, April 2024.
    BibTeX arXiv
    • @inproceedings{Yan2024apr,
    • author = {Yan, Jiaqi and Chakrabarty, Ankush and Rupenyan, Alisa and Lygeros, John}},
    • title = {MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models},
    • booktitle = {arXiv},
    • year = 2024,
    • month = apr,
    • url = {https://arxiv.org/abs/2404.12097}
    • }
  •  Jeon, W., Chakrabarty, A., Zemouche, A., Rajamani, R., "LMI-Based Neural Observer for State and Nonlinear Function Estimation", International Journal of Robust and Nonlinear Control, DOI: 10.1002/​rnc.7327, April 2024.
    BibTeX TR2024-036 PDF
    • @article{Jeon2024apr,
    • author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
    • title = {LMI-Based Neural Observer for State and Nonlinear Function Estimation},
    • journal = {International Journal of Robust and Nonlinear Control},
    • year = 2024,
    • month = apr,
    • doi = {10.1002/rnc.7327},
    • url = {https://www.merl.com/publications/TR2024-036}
    • }
  •  Shao, K., Romeres, D., Chakrabarty, A., Mesbah, A., "Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine", Advances in Neural Information Processing Systems (NeurIPS), December 2023.
    BibTeX TR2023-146 PDF
    • @inproceedings{Shao2023dec,
    • author = {Shao, Ketong and Romeres, Diego and Chakrabarty, Ankush and Mesbah, Ali},
    • title = {Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine},
    • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    • year = 2023,
    • month = dec,
    • url = {https://www.merl.com/publications/TR2023-146}
    • }
  •  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}
    • }
  •  Paulson, J.A., Sorouifar, F., Laughman, C.R., Chakrabarty, A., "Self-optimizing vapor compression cycles online with Bayesian optimization under local search region constraints", ASME Journal of Dynamic Systems, Measurements, and Control, DOI: 10.1115/​1.4064027, September 2023.
    BibTeX TR2023-122 PDF
    • @article{Paulson2023sep,
    • author = {Paulson, Joel A. and Sorouifar, Farshud and Laughman, Christopher R. and Chakrabarty, Ankush},
    • title = {Self-optimizing vapor compression cycles online with Bayesian optimization under local search region constraints},
    • journal = {ASME Journal of Dynamic Systems, Measurements, and Control},
    • year = 2023,
    • month = sep,
    • doi = {10.1115/1.4064027},
    • url = {https://www.merl.com/publications/TR2023-122}
    • }
  •  Zhan, S., Chakrabarty, A., Laughman, C.R., Chong, A., "A Virtual Testbed for Robust and Reproducible Calibration of Building Energy Simulation Models", IBPSA Building Simulation Conference, DOI: 10.26868/​25222708.2023.1482, September 2023.
    BibTeX TR2023-114 PDF
    • @inproceedings{Zhan2023sep,
    • author = {{Zhan, Sicheng and Chakrabarty, Ankush and Laughman, Christopher R. and Chong, Adrian}},
    • title = {A Virtual Testbed for Robust and Reproducible Calibration of Building Energy Simulation Models},
    • booktitle = {IBPSA Building Simulation Conference},
    • year = 2023,
    • month = sep,
    • doi = {10.26868/25222708.2023.1482},
    • url = {https://www.merl.com/publications/TR2023-114}
    • }
  •  Laughman, C.R., Deshpande, V.M., Qiao, H., Bortoff, S.A., Chakrabarty, A., "Digital Twins for Vapor Compression Cycles: Challenges & Opportunities", International Congress of Refrigeration (ICR), DOI: 10.18462/​iir.icr.2023.0765, August 2023.
    BibTeX TR2023-103 PDF
    • @inproceedings{Laughman2023aug,
    • author = {Laughman, Christopher R. and Deshpande, Vedang M. and Qiao, Hongtao and Bortoff, Scott A. and Chakrabarty, Ankush},
    • title = {Digital Twins for Vapor Compression Cycles: Challenges & Opportunities},
    • booktitle = {International Congress of Refrigeration (ICR)},
    • year = 2023,
    • month = aug,
    • publisher = {INTERNATIONAL INSTITUTE OF REFRIGERATION},
    • doi = {10.18462/iir.icr.2023.0765},
    • issn = {0151-1637},
    • isbn = {978-2-36215-055-5},
    • url = {https://www.merl.com/publications/TR2023-103}
    • }
  •  Chakrabarty, A., Vinod, A.P., Mansour, H., Bortoff, S.A., Laughman, C.R., "Moving Horizon Estimation for Digital Twins using Deep Autoencoders", World Congress of the International Federation of Automatic Control (IFAC), Ishii, H. and Ebihara, Y. and Imura, J. and Yamakita, M., Eds., DOI: 10.1016/​j.ifacol.2023.10.207, July 2023, pp. 5500-5505.
    BibTeX TR2023-088 PDF
    • @inproceedings{Chakrabarty2023jul2,
    • author = {Chakrabarty, Ankush and Vinod, Abraham P. and Mansour, Hassan and Bortoff, Scott A. and Laughman, Christopher R.},
    • title = {Moving Horizon Estimation for Digital Twins using Deep Autoencoders},
    • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
    • year = 2023,
    • editor = {Ishii, H. and Ebihara, Y. and Imura, J. and Yamakita, M.},
    • pages = {5500--5505},
    • month = jul,
    • publisher = {Elseiver},
    • doi = {10.1016/j.ifacol.2023.10.207},
    • url = {https://www.merl.com/publications/TR2023-088}
    • }
  •  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}
    • }
  •  Chinchilla, R., Deshpande, V.M., Chakrabarty, A., Laughman, C.R., "Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles", American Control Conference (ACC), DOI: 10.23919/​ACC55779.2023.10155954, May 2023, pp. 4069-4076.
    BibTeX TR2023-051 PDF
    • @inproceedings{Chinchilla2023may,
    • author = {Chinchilla, Raphael and Deshpande, Vedang M. and Chakrabarty, Ankush and Laughman, Christopher R.},
    • title = {Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles},
    • booktitle = {American Control Conference (ACC)},
    • year = 2023,
    • pages = {4069--4076},
    • month = may,
    • publisher = {IEEE},
    • doi = {10.23919/ACC55779.2023.10155954},
    • issn = {2378-5861},
    • isbn = {978-1-6654-6952-4},
    • url = {https://www.merl.com/publications/TR2023-051}
    • }
  •  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}
    • }