Publications

2024

  • [1]
    Learning Networked Dynamical System Models with Weak Form and Graph Neural Networks.
    By Yu, Y., Huang, D., Park, S. and Pangborn, H.
    In arXiv preprint arXiv:2407.16779, 2024.
  • [2]
    Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability.
    By Yu, Y., Li, P., Huang, D. and Sharma, A.
    In Urban Climate, vol. 56, p. 102003, 2024.
  • [3]
    Learning Coarse-Grained Dynamics on Graph.
    By Yu, Y., Harlim, J., Huang, D. and Li, Y.
    In arXiv preprint arXiv:2405.09324, 2024.
  • [4]
    Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids.
    By Jiang, X., Li, Y. and Huang, D.
    In IEEE Transactions on Smart Grid, 2024.
  • [5]
    Modal Analysis of Spatiotemporal Data via Multivariate Gaussian Process Regression.
    By Song, J. and Huang, D.
    In arXiv preprint arXiv:2403.13118, 2024.
  • [6]
    Control-Theoretic Physics-Infused Reduced-Order Modeling with Infinite-Dimensional Parametric Inputs.
    By Vargas Venegas, C.A. and Huang, D.
    In SIAM UQ242024.
  • [7]
    Parametrized Global Linearization Models for Flutter Prediction.
    By Song, J., Yu, Y. and Huang, D.
    In AIAA SCITECH 2024 Forum2024.
  • [8]
    Residual Dynamic Mode Decomposition with Control for Nonlinear Aeroservoelastic Applications.
    By Rains, J.C., Huang, D. and Wang, Y.
    In AIAA SCITECH 2024 Forum2024.
  • [9]
    Development of Weak-Form Physics-Infused Reduced-Order Modeling With Applications.
    By Vargas Venegas, C.A. and Huang, D.
    In AIAA SCITECH 2024 Forum2024.
  • [10]
    Evaluation of Shock Wave-Boundary Layer Interaction Modeling Capabilities for Use in a Hypersonic Aerothermoelastic Framework.
    By Kimmel, E., Huang, D., Sharma, V., Singh, J., Raman, V. and Friedmann, P.P.
    In AIAA SCITECH 2024 Forum2024.
  • [11]
    Reduced-Order Modeling for Fluid-Thermal-Structural Interaction of Cone-Slice-Ramp in High-Speed Flows.
    By Sadagopan, A. and Huang, D.
    In AIAA SCITECH 2024 Forum2024.
  • [12]
    A Convex Approach to High-fidelity Landing Trajectory Optimization for Advanced Air Mobility.
    By Wu, Y., Deniz, S., Wang, Z. and Huang, D.
    In AIAA SCITECH 2024 Forum2024.
  • [13]
    Data-Driven Modeling and Prediction of Transient Dynamics of Microgrids through Dynamic Mode Decomposition with Control.
    By Jiang, X., Li, Y. and Huang, D.
    In 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5, 2024.
  • [14]
    PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement.
    By Yu, Y., Jiang, X., Huang, D. and Li, Y.
    In IEEE Access, 2024.
  • [15]
    Touchless underwater wall-distance sensing via active proprioception of a robotic flapper.
    By Panta, K., Deng, H., Zhang, Z., Huang, D., Panah, A. and Cheng, B.
    In Bioinspiration & Biomimetics, vol. 19, no. 2, p. 026009, 2024.

    • 2023

  • [1]
    Structural Dynamics.
    By Friedmann, P.P., Lesieutre, G.A. and Huang, D.
    Cambridge University Press, 2023
  • [2]
    Learning Koopman Operators with Control Using Bi-level Optimization.
    By Huang, D., Prasetyo, M.B., Yu, Y. and Geng, J.
    In IEEE CDC 20232023.
  • [3]
    Hierarchical Multi-Layered Sparse Identification for Prediction of Non-Linear Dynamics of Reconfigurable Microgrids.
    By Nandakumar, A., Li, Y. and Huang, D.
    In 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–6, 2023.
  • [4]
    Graph-based Hierarchical Control of Electrified Aircraft Systems with Automated Timescale Decomposition.
    By Yu, Y., Park, S., Huang, D. and Pangborn, H.
    In AIAA AVIATION 2023 Forum2023.
  • [5]
    Modal Analysis of Spatiotemporal Data via Multi-fidelity Multi-variate Gaussian Processes.
    By Song, J. and Huang, D.
    In AIAA AVIATION 2023 Forum2023.
  • [6]
    Precision Landing Trajectory Optimization for eVTOL Vehicles with High-Fidelity Aerodynamic Models.
    By Wu, Y., Deniz, S., Shi, Y., Wang, Z. and Huang, D.
    In AIAA AVIATION 2023 Forum2023.
  • [7]
    PyPose v0.6: The Imperative Programming Interface for Robotics.
    By Zhan, Z., Li, X., Li, Q., He, H., Xiao, H., Xu, Y., Chen, X., Xu, K., Cao, K., Huang, D. and others.
    In arXiv preprint arXiv:2309.13035, 2023.
  • [8]
    PyPose: A Library for Robot Learning With Physics-Based Optimization.
    By Wang, C., Gao, D., Xu, K., Geng, J., Hu, Y., Qiu, Y., Li, B., Yang, F., Moon, B., Huang, D. and others.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22024–22034, 2023.
  • [9]
    Physics-Infused Reduced-Order Modeling of Aerothermal Loads for Hypersonic Aerothermoelastic Analysis.
    By Vargas Venegas, C.A. and Huang, D.
    In AIAA Journal, 2023.
  • [10]
    Hypersonic Fluid-Thermal-Structural Interaction of Cone-Slice-Ramp: Computations with Experimental Validation.
    By Sadagopan, A., Huang, D., Jirasek, A., Seidel, J., Pandey, A. and Casper, K.M.
    In AIAA Journal, vol. 61, no. 11, pp. 4752–4771, 2023.
  • [11]
    Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models.
    By Coulter, B.G., Huang, D. and Wang, Z.
    In Journal of Aircraft, vol. 60, no. 3, pp. 870–882, 2023.
  • [12]
    Regional heatwave prediction using Graph Neural Network and weather station data.
    By Li, P., Yu, Y., Huang, D., Sharma, A. and Wang, Z.-H.
    In Geophysical Research Letters, 2023.
  • [13]
    Design of an Aeroelastically Scaled Model in a Compressible Air Wind Tunnel Facility Using Multifidelity Multi-Objective Bayesian Optimization.
    By Huang, D., Renganathan, A. and Miller, M.
    In AIAA SCITECH 2023 Forum2023.
  • [14]
    Optimal Landing Control of eVTOL Vehicles Using ODE-Based Aerodynamic Model.
    By Wang, Z., Wu, Y. and Huang, D.
    In AIAA SCITECH 2023 Forum2023.

    • 2022

  • [1]
    Modal analysis of a shear layer in high-supersonic cavity flows using data-driven and operator-based resolvent analysis.
    By Sadagopan, A., Huang, D., Safari, M. and Yeh, C.-A.
    In Bulletin of the American Physical Society2022.
  • [2]
    Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models.
    By Coulter, B.G., Huang, D. and Wang, Z.
    In Journal of Aircraft, pp. 1–13, 2022.
  • [3]
    PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement.
    By Yu, Y., Jiang, X., Huang, D. and Li, Y.
    In arXiv preprint arXiv:2204.08557, 2022.
  • [4]
    Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids.
    By Jiang, X., Li, Y. and Huang, D.
    In arXiv preprint arXiv:2205.03214, 2022.
  • [5]
    Study of fluid–thermal–structural interaction in high-temperature high-speed flow using multi-fidelity multi-variate surrogates.
    By Huang, D., Sadagopan, A., Düzel, Ü. and Hanquist, K.M.
    In Journal of Fluids and Structures, vol. 113, p. 103682, 2022.
  • [6]
    Reduced-Order Modeling of Ship Airwakes with Atmospheric Turbulence Effects using Dynamic Graph Networks.
    By Yu, Y., Major, D.A., Huang, D. and Schmitz, S.
    In AIAA SCITECH 2022 Forum, p. 2533, 2022.
  • [7]
    Koopman Operators for Bifurcation Analysis in Hypersonic Aerothermoelasticity.
    By Guého, D., Macchio, G.R., Huang, D. and Singla, P.
    In AIAA SCITECH 2022 Forum, p. 0655, 2022.
  • [8]
    Physics-Infused Reduced Order Modeling of Hypersonic Aerothermal Loads for Aerothermoelastic Analysis.
    By Vargas Venegas, C.A. and Huang, D.
    In AIAA SCITECH 2022 Forum, p. 0989, 2022.
  • [9]
    Geometric Design of Hypersonic Vehicles for Optimal Mission Performance using Machine Learning.
    By Coulter, B., Wang, Z. and Huang, D.
    In AIAA SCITECH 2022 Forum, p. 1304, 2022.
  • [10]
    An Experimental and Computational Correlation Study for Fluid-Thermal-Structural Interaction of a Control Surface in Hypersonic Flow.
    By Sadagopan, A., Huang, D., Jirasek, A., Seidel, J., Pandey, A. and Casper, K.M.
    In AIAA SCITECH 2022 Forum, p. 0291, 2022.

    • 2021

  • [1]
    Applications of Gaussian Process Regression in the Aero-Thermo-Servo-Elastic Analysis Towards Integrated Hypersonic Flight Dynamic Analysis.
    By Huang, D.
    In 2021 60th IEEE Conference on Decision and Control (CDC), pp. 6–15, 2021.
  • [2]
    Resilience analysis of cyber-physical networked microgrids with communication latency.
    By Li, Y., Huang, D., Zhang, Y. and Orekan, T.
    In 2021 IEEE Power & Energy Society General Meeting (PESGM), pp. 1–5, 2021.
  • [3]
    Multi-Variate Gaussian Process Regression for Angles-Only Initial Orbit Determination.
    By Schwab, D., Singla, P. and Huang, D.
    In AAS/AIAA Astrodynamics Specialist Conference, 2020, pp. 3077–3096, 2021.
  • [4]
    Assessment of high-temperature effects on hypersonic aerothermoelastic analysis using multi-fidelity multi-variate surrogates.
    By Sadagopan, A., Huang, D., Duzel, U., Martin, L.E. and Hanquist, K.M.
    In AIAA Scitech 2021 Forum, p. 1610, 2021.
  • [5]
    Time-varying linear reduced order model for hypersonic aerothermoelastic analysis.
    By Guého, D., Singla, P. and Huang, D.
    In AIAA Scitech 2021 Forum, p. 1706, 2021.
  • [6]
    Expedient hypersonic aerothermal prediction for aerothermoelastic analysis via field inversion and machine learning.
    By Vargas Venegas, C.A. and Huang, D.
    In AIAA Scitech 2021 Forum, p. 1707, 2021.
  • [7]
    Hypersonic Trajectory Optimization with High-Fidelity Aerothermodynamic Models.
    By Coulter, B., Wang, Z., Huang, D. and others.
    In AIAA Scitech 2021 Forum, p. 0715, 2021.
  • [8]
    Sparse Nonlinear System Identification for Hypersonic Aerothermoelastic Analysis with Stochastic Loads.
    By Guého, D., Singla, P. and Huang, D.
    In AIAA Scitech 2021 Forum, p. 1609, 2021.
  • [9]
    Numerical Investigation of Fluid-Thermal-Structural Interaction for a Control Surface in Hypersonic Flow.
    By Sadagopan, A., Huang, D., Xu, H. and Yang, X.I.
    In AIAA Scitech 2021 Forum, p. 0911, 2021.
  • [10]
    Identifying HOPF bifurcations of networked microgrids induced by the integration of EV charging stations.
    By Jiang, X., Li, Y., Du, L. and Huang, D.
    In 2021 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 690–694, 2021.
  • [11]
    Modeling of the blade crossover interaction using machine learning.
    By Sharma, K., Surenderan, V., Yu, Y., Huang, D., Brentner, K. and Anusonti-Inthra, P.
    In 77th Annual Vertical Flight Society Forum and Technology Display: The Future of Vertical Flight, FORUM 20212021.

    • 2020

  • [1]
    Impact of high-temperature effects on the aerothermoelastic behavior of composite skin panels in hypersonic flow.
    By Sadagopan, A., Huang, D. and Hanquist, K.M.
    In AIAA Scitech 2020 Forum, p. 0937, 2020.
  • [2]
    An aerothermoelastic analysis framework with reduced-order modeling applied to composite panels in hypersonic flows.
    By Huang, D. and Friedmann, P.P.
    In Journal of Fluids and Structures, vol. 94, 2020.
  • [3]
    Multi-Objective Optimization Framework for Hypersonic Aerothermoelastic Scaling Laws and Its Application.
    By Huang, D. and Friedmann, P.P.
    In AIAA Journal, vol. 58, no. 7, pp. 3250–3257, 2020.

    • 2019

  • [1]
    A Surrogate-Based Optimization Framework for Hypersonic Aerothermoelastic Scaling Laws with Application to Skin Panels.
    By Huang, D. and Friedmann, P.P.
    In 16th Dynamics Specialists Conference, pp. 1–27, 2019.
  • [2]Development of a Hypersonic Aerothermoelastic Framework and Its Application to Flutter and Aerothermoelastic Scaling of Skin Panels, PhD thesis, 2019
  • [3]
    A multi-objective optimization framework for hypersonic aerothermoelastic scaling laws and its application to skin panels.
    By Huang, D. and Friedmann, P.P.
    In International Forum on Aeroelasticity and Structural Dynamicsno. 2019-143, , pp. 1–44, 2019.

    • 2018

  • [1]
    Aerothermoelastic Scaling Laws for Hypersonic Skin Panel Configurations with Arbitrary Flow Orientation.
    By Huang, D., Rokita, T. and Friedmann, P.P.
    In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp. 1–23, 2018.
  • [2]
    An Integrated Aerothermoelastic Analysis Framework With Application to Skin Panels.
    By Huang, D., Rokita, T. and Friedmann, P.P.
    In AIAA Journal, vol. 56, no. 11, pp. 4562–4581, 2018.
  • [3]
    Aerothermoelastic Scaling Laws for Hypersonic Skin Panel Configurations with Arbitrary Flow Orientation.
    By Huang, D., Friedmann, P.P. and Rokita, T.
    In AIAA Journal, 2018.
  • [4]
    Efficient Modeling of Fluid-Structure-Thermal Interaction in Hypersonic Flow.
    By Rokita, T., Huang, D. and Friedmann, P.P.
    In 58th Israel Annual Conference on Aerospace Sciences2018.

    • Before 2018

  • [1]
    Efficient Reduced-Order Modeling for Skin Panels in Hypersonic Flow and Its Application to Generating Aerothermoelastic Scaling Laws.
    By Huang, D., Rokita, T. and Friedmann, P.P.
    In International Forum on Aeroelasticity and Structural Dynamics2017.
  • [2]
    An Aerothermoelastic Analysis Framework Enhanced by Model Order Reduction With Applications.
    By Huang, D., Rokita, T. and Friedmann, P.P.
    In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp. 1–19, 2017.
  • [3]
    An Integrated Aerothermoelastic Analysis Framework for Predicting the Response of Composite Panels.
    By Huang, D. and Friedmann, P.P.
    In 15th Dynamics Specialists Conference, pp. 1–37, 2016.