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“Climate change necessitates innovations in computational modeling to improve the design and operation of infrastructure systems, which are impacted by, and in turn impact, climate change.”
Vision statement
Vision statement
Climate change, the defining challenge of our time, necessitates innovations in computational modeling to improve the design and operation of infrastructure systems, which are impacted by, and in turn impact, climate change. The increasing severity and frequency of extreme weather events directly affect safety and resilience. At the same time, the full life cycle of infrastructure—from design and material manufacturing to construction, usage, and demolition—accounts for approximately 40 percent of global emissions, making us one of the highest-emitting sectors.
The EISS Lab @ Hongik University is committed to advancing computational frameworks that support both climate adaptation and mitigation. Our mission is to enhance the resilience of infrastructure while accelerating the deployment of renewable energy systems. We develop efficient algorithms that improve the prediction and optimization of system lifespans and energy generation, all while reducing computational burden.
By integrating computational mechanics, macro energy system modeling, and AI-driven enhancement and acceleration, we take a multidisciplinary approach centered on key thrusts.
Thrust 1. Design for Climate
Thrust 1. Design for Climate
We develop advanced risk assessment methodologies to model nonstationary climate processes, enabling more accurate prediction of future climate-related risks compared to traditional approaches. Leveraging AI-driven data science techniques, we build computational frameworks that are both time- and data-efficient, allowing for rapid estimation of long-term environmental impacts and life cycle assessments (LCA) in large-scale renewable energy systems, such as floating offshore wind farms.
Thrust 2. O&M Optimization
Thrust 2. O&M Optimization
We investigate multi-agent reinforcement learning (MARL) for optimizing operations and maintenance (O&M) scheduling across wind farms, solar farms, and multi-sectoral energy systems. Each asset, crew, and vessel acts as an agent with partial information, making centralized scheduling intractable at realistic scale. Our goal is to learn adaptive policies that minimize O&M cost and downtime while maximizing crew safety under uncertain weather and degradation conditions.
Thrust 3. Remote Diagnosis
Thrust 3. Remote Diagnosis
We develop remote sensing–based frameworks that assess the structural health of large-scale infrastructure without on-site expert inspection or dedicated sensor networks. Using satellite SAR interferometry (PS-InSAR, MiaplPy), we extract millimeter-scale displacement histories of bridges and, moving forward, of offshore wind turbines and other distributed energy infrastructure. By combining these observations with physics-based structural models and statistical learning, we aim to separate thermal, tidal, and load-induced responses from irreversible degradation—turning freely available satellite archives into a scalable, low-cost monitoring capability.
Thrust 4. IDS of MES
Thrust 4. IDS of MES
We develop computational models to assess the role of emerging clean energy technologies—such as floating wind turbines, floating PVs and thermal energy storage systems—alongside established resources including onshore solar, wind, and hydropower. Applied within macro-energy systems modeling, these frameworks identify optimal configurations for generation assets and long-duration energy storage (LDES). Our work focuses on evaluating the techno-economic feasibility of diverse technology portfolios to support data-driven decision-making for future large-scale energy system deployment.
Research Questions
Research Questions
Q1. How can we perform dynamic structural analysis of complex energy infrastructure systems faster and more accurately?
Q2. How can we design, operate, and maintain infrastructure systems to extend their service life while reducing carbon footprints?
Q3. How much renewable energy generation and energy storage capacity should be built, and where should these facilities be located?
Q4. How can we assess the health of large-scale infrastructure remotely, without field inspection or dense sensor networks?
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