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title: Home
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<h1>Taehwa Hong</h1>
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<pclass="lead">Ph.D. Candidate in Mechanical Engineering | Robotics Simulation Researcher</p>
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<h2>About Me</h2>
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<p>I am a Ph.D. candidate at Seoul National University (expected February 2026) specializing in <strong>physics-based simulation</strong> and <strong>data generation for robotic learning</strong>. My research addresses the <strong>fidelity-tractability trade-off</strong> in simulating complex physical interactions, particularly involving <strong>deformable structures</strong> and <strong>contact dynamics</strong>.</p>
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<p>To bridge this gap, I develop and validate <strong>abstraction methodologies</strong>, progressing from analytical models for real-time control, to data-driven surrogate models enabling reinforcement learning, and towards Neural Physics Engines (NPEs) for high-fidelity contact modeling. My goal is to create the foundational simulation tools and pipelines needed to generate high-quality, physically-grounded data for training the next generation of physically intelligent robots.</p>
<li><strong>Integrated M.S./Ph.D. in Mechanical Engineering</strong> (Expected Feb 2026)<br>Seoul National University</li>
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<li><strong>B.S. in Mechanical and Aerospace Engineering</strong> (Feb 2019)<br>Seoul National University</li>
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<h2>Technical Skills</h2>
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<ul>
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<li><strong>Simulation & Modeling</strong>: SOFA (FEM), PyBullet, Isaac Sim, MuJoCo, ABAQUS; Contact Mechanics, Deformable Body Dynamics, Model Order Reduction (MOR)</li>
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