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Christopher R. Laughman

Christopher R. Laughman
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  • 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 R. 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 A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Daniel N. Nikovski; Rien Quirynen; Diego Romeres; William S. 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

    •  Rackauckas, C., Anantharaman, R., Edelman, A., Gowda, S., Gwozdz, M., Jain, A., Laughman, C.R., Ma, Y., Martinuzzi, F., Pal, A., Rajput, U., Saba, E., Shah, V., "Composing Modeling and Simulation with Machine Learning in Julia", 14th International Modelica Conference, September 2021.
      BibTeX TR2021-114 PDF
      • @inproceedings{Rackauckas2021sep,
      • author = {Rackauckas, Chris and Anantharaman, Ranjan and Edelman, Alan and Gowda, Shashi and Gwozdz, Maja and Jain, Anand and Laughman, Christopher R. and Ma, Yingbo and Martinuzzi, Francesco and Pal, Avik and Rajput, Utkarsh and Saba, Elliot and Shah, Viral},
      • title = {Composing Modeling and Simulation with Machine Learning in Julia},
      • booktitle = {14th International Modelica Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-114}
      • }
    •  Chakrabarty, A., Maddalena, E., Qiao, H., Laughman, C.R., "Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization", 2021 Building Simulation Conference, September 2021.
      BibTeX TR2021-105 PDF Video
      • @inproceedings{Chakrabarty2021sep,
      • author = {Chakrabarty, Ankush and Maddalena, Emilio and Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization},
      • booktitle = {2021 Building Simulation Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-105}
      • }
    •  Qiao, H., Laughman, C.R., "Modeling and Analysis of Pressure Drop Oscillations in Horizontal Boiling Flow", International Refrigeration and Air Conditioning Conference at Purdue, August 2021.
      BibTeX TR2021-102 PDF
      • @inproceedings{Qiao2021aug,
      • author = {Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Modeling and Analysis of Pressure Drop Oscillations in Horizontal Boiling Flow},
      • booktitle = {International Refrigeration and Air Conditioning Conference at Purdue},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-102}
      • }
    •  Chakrabarty, A., Wichern, G., Laughman, C.R., "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", International Conference on Machine Learning (ICML), July 2021.
      BibTeX TR2021-086 PDF
      • @inproceedings{Chakrabarty2021jul,
      • 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},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2021,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2021-086}
      • }
    •  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}
      • }
    See All Publications for Chris

  • 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
    See All Patents for MERL