Christopher Laughman

Christopher Laughman
  • Biography

    Christopher's interests lie in the intersection of the modeling of physical systems and the experimental construction and testing of these systems, including simulation, numerical methods, and fault detection. He has worked on a variety of multi-physical systems, such as thermo-fluid systems and electromechanical energy conversion systems.

  • Recent News & Events

    •  TALK   Universal Differential Equations for Scientific Machine Learning
      Date & Time: Thursday, May 7, 2020; 12:00 PM
      Speaker: Christopher Rackauckas, MIT
      MERL Host: Christopher Laughman
      Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
    •  
    •  NEWS   MERL researchers presented more than 8 papers in European Control Conference, ECC 2019
      Date: June 25, 2019 - June 28, 2019
      Where: Naples, Italy
      MERL Contacts: Karl Berntorp; Scott Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh Jha; Christopher Laughman; Daniel Nikovski; Rien Quirynen; Diego Romeres; William Yerazunis
      Research Areas: Control, Machine Learning, Optimization
      Brief
      • The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.
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  • MERL Publications

    •  Chakrabarty, A., Wichern, G., Laughman, C.R., "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", arXiv, June 2021.
      BibTeX arXiv
      • @article{Chakrabarty2021jun,
      • author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
      • title = {ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins},
      • journal = {arXiv},
      • year = 2021,
      • month = jun,
      • url = {https://arxiv.org/abs/2106.15502}
      • }
    •  Laughman, C.R., Qiao, H., "Patch-based Thermodynamic Property Models for the Subcritical Region", Purdue Air-Conditioning and Refrigeration Conference, May 2021.
      BibTeX TR2021-053 PDF
      • @inproceedings{Laughman2021may,
      • author = {Laughman, Christopher R. and Qiao, Hongtao},
      • title = {Patch-based Thermodynamic Property Models for the Subcritical Region},
      • booktitle = {Purdue Air-Conditioning and Refrigeration Conference},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-053}
      • }
    •  Anantharaman, R., Ma, Y., Gowda, S., Laughman, C.R., Shah, V., Edelman, A., Rackauckas, C., "Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks", AAAI Spring Symposium on Combining Artificial Intelligence with Physical Sciences, March 2021.
      BibTeX TR2021-021 PDF
      • @inproceedings{Anantharaman2021mar,
      • author = {Anantharaman, Ranjan and Ma, Yingbo and Gowda, Shashi and Laughman, Christopher R. and Shah, Viral and Edelman, Alan and Rackauckas, Chris},
      • title = {Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks},
      • booktitle = {AAAI Spring Symposium on Combining Artificial Intelligence with Physical Sciences},
      • year = 2021,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2021-021}
      • }
    •  Anantharaman, R., Ma, Y., Gowda, S., Laughman, C.R., Shah, V., Edelman, A., Rackauckas, C., "Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks", Advances in Neural Information Processing Systems (NeurIPS), December 2020.
      BibTeX TR2020-169 PDF
      • @inproceedings{Anantharaman2020dec,
      • author = {Anantharaman, Ranjan and Ma, Yingbo and Gowda, Shashi and Laughman, Christopher R. and Shah, Viral and Edelman, Alan and Rackauckas, Chris},
      • title = {Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-169}
      • }
    •  Laughman, C.R., Bortoff, S.A., "Nonlinear State Estimation with FMI: Tutorial and Applications", American Modelica Conference 2020, Tiller, M. and Tummescheit, H. and Vanfretti, L. and Laughman, C. and Wetter, M., Eds., DOI: 10.3384/​ECP20169186, March 2020, pp. 186-195.
      BibTeX TR2020-031 PDF Software
      • @inproceedings{Laughman2020mar,
      • author = {Laughman, Christopher R. and Bortoff, Scott A.},
      • title = {Nonlinear State Estimation with FMI: Tutorial and Applications},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • editor = {Tiller, M. and Tummescheit, H. and Vanfretti, L. and Laughman, C. and Wetter, M.},
      • pages = {186--195},
      • month = mar,
      • publisher = {Link√∂ping University Electronic Press},
      • doi = {10.3384/ECP20169186},
      • issn = {1650-3686},
      • isbn = {978-91-7929-900-2},
      • url = {https://www.merl.com/publications/TR2020-031}
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Power Optimizing Control of Multi-Zone Heat Pumps"
      Inventors: Bortoff, Scott A.; Burns, Dan J; Laughman, Christopher; Qiao, Hongtao
      Patent No.: 10,895,412
      Issue Date: Jan 19, 2021
    • Title: "System and Method for Controlling Refrigerant in Vapor Compression System"
      Inventors: Laughman, Christopher; Qiao, Hongtao; Burns, Dan J; Bortoff, Scott A.
      Patent No.: 10,830,515
      Issue Date: Nov 10, 2020
    • Title: "System and Method for Thermal Comfort Control"
      Inventors: Laughman, Christopher; Bortoff, Scott A.
      Patent No.: 10,767,887
      Issue Date: Sep 8, 2020
    • Title: "System and Method for Controlling Vapor Compression Systems"
      Inventors: Burns, Dan J; Laughman, Christopher; Bortoff, Scott A.
      Patent No.: 10,495,364
      Issue Date: Dec 3, 2019
    • Title: "Coordinated Operation of Multiple Space-Conditioning Systems"
      Inventors: Laughman, Christopher; Qiao, Hongtao; Burns, Dan J; Bortoff, Scott A.
      Patent No.: 10,234,158
      Issue Date: Mar 19, 2019
    • Title: "System and Method for Controlling Multi-Zone Vapor Compression Systems"
      Inventors: Burns, Dan J; Di Cairano, Stefano; Bortoff, Scott A.; Laughman, Christopher
      Patent No.: 10,174,957
      Issue Date: Jan 8, 2019
    • Title: "System and Method for Controlling of Vapor Compression System"
      Inventors: Burns, Dan J; Jain, Neera; Laughman, Christopher; Di Cairano, Stefano; Bortoff, Scott A.
      Patent No.: 9,625,196
      Issue Date: Apr 18, 2017
    • Title: "Method For Reconstructing 3D Scenes From 2D Images"
      Inventors: Ramalingam, Srikumar; Taguchi, Yuichi; Pillai, Jaishanker K; Burns, Dan J; Laughman, Christopher
      Patent No.: 9,595,134
      Issue Date: Mar 14, 2017
    • Title: "System and Method for Controlling Vapor Compression Systems"
      Inventors: Burns, Dan J; Laughman, Christopher; Bortoff, Scott A.
      Patent No.: 9,534,820
      Issue Date: Jan 3, 2017
    • Title: "System and Method for Controlling Temperature and Humidity in Multiple Spaces using Liquid Desiccant"
      Inventors: Laughman, Christopher; Burns, Dan J; Bortoff, Scott A.; Waters, Richard C.
      Patent No.: 9,518,765
      Issue Date: Dec 13, 2016
    • Title: "Adaptive Control of Vapor Compression System"
      Inventors: Burns, Dan J; Laughman, Christopher
      Patent No.: 9,182,154
      Issue Date: Nov 10, 2015
    • Title: "Controlling Operation of Vapor Compression System"
      Inventors: Nikovski, Daniel N.; Laughman, Christopher; Burns, Dan J
      Patent No.: 8,793,003
      Issue Date: Jul 29, 2014
    • Title: "System and Method for Controlling Operations of Vapor Compression"
      Inventors: Bortoff, Scott A.; Burns, Dan J; Laughman, Christopher
      Patent No.: 8,694,131
      Issue Date: Apr 8, 2014
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