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@KHU-MASLAB

Modeling & Simulation Lab.

Department of Mechanical Engineering (Integrated Engineering), Kyung Hee University
README.md

Welcome to Modeling and Simulation (M&S) lab (Jin-Gyun Kim's group)

We are in Department of Mechanical Engineering at Kyung Hee University. We develop computational modeling and simulation methods of structures based on classical mechanics, in particular, for dynamics and vibration. Our current academic focus is to develop numerical methods for the following issues:

  • Deterministic/stochastic M&S
  • Multi-physics M&S
  • Data driven M&S (In particular, for digital twin, virtual sensing and real-time simulation)
  • Engineering applications

We also tackle broad range projects of vibration, dynamics and CAE issues with both academic and industrial partners. For more information, please visit our website.

Hits

Pinned

  1. cNN-DP cNN-DP Public

    A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives

    Python 1

  2. RecurDynPython RecurDynPython Public

    RecurDyn automation using Python and ProcessNet

    Python 2

  3. TimeSeriesSeq2Seq TimeSeriesSeq2Seq Public

    Sequence-to-sequence model implementations including RNN, CNN, Attention, and Transformers using PyTorch

    Python 1

  4. ROS-SLAM-AppleSilicon ROS-SLAM-AppleSilicon Public

    ROS with Turtlebot3 SLAM Simulation installation for Apple Silicon (M1, M2) users

    Shell

Repositories

Showing 7 of 7 repositories
  • ROS-SLAM-AppleSilicon Public

    ROS with Turtlebot3 SLAM Simulation installation for Apple Silicon (M1, M2) users

    Shell 0 MIT 0 0 0 Updated Oct 30, 2023
  • RecurDynPython Public

    RecurDyn automation using Python and ProcessNet

    Python 2 MIT 0 1 0 Updated Sep 17, 2023
  • TimeSeriesSeq2Seq Public

    Sequence-to-sequence model implementations including RNN, CNN, Attention, and Transformers using PyTorch

    Python 1 MIT 0 0 0 Updated Aug 28, 2023
  • cNN-DP Public

    A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives

    Python 1 MIT 0 0 0 Updated Jun 12, 2023
  • cNN-DP_deprecated Public

    cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.

    Python 1 MIT 0 0 0 Updated Feb 7, 2023
  • DNN_Basic Public

    Basic level DNN codes with PyTorch.

    Python 0 MIT 0 0 0 Updated Feb 6, 2023
  • .github Public
    0 0 0 0 Updated Nov 30, 2022

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