Our project experience connects various data-driven methods with industrial data across petrochemicals, materials, equipment, batteries, and energy systems. 석유화학, 소재, 설비, 배터리, 에너지 시스템 분야에서 다양한 데이터 기반 방법론들을 실제 산업 데이터에 연결하여 문제를 해결합니다.
AI-based reduced-order modeling for electrode manufacturing, including data curation, ROM development, and AI pipeline construction. AI-based reduced-order modeling for electrode manufacturing, including data curation, ROM development, and AI pipeline construction.
Data curation and temperature-conditioned deep-learning architectures for solvent–solute systems. Data curation and temperature-conditioned deep-learning architectures for solvent–solute systems.
Industrial data preprocessing, predictive modeling, optimization, and digital-transformation workflows. Industrial data preprocessing, predictive modeling, optimization, and digital-transformation workflows.
Predictive modeling, material analysis, inverse design, and recipe optimization for customized lubricants. Predictive modeling, material analysis, inverse design, and recipe optimization for customized lubricants.
AI modeling and feature engineering for VLE prediction of blended CO₂ absorbents. AI modeling and feature engineering for VLE prediction of blended CO₂ absorbents.
Industrial AI pipeline and GUI development for customized lubricant design. Industrial AI pipeline and GUI development for customized lubricant design.
Control-valve size prediction and explainable AI for engineering automation. Control-valve size prediction and explainable AI for engineering automation.
Property prediction for polymer composites and application-oriented GUI development. Property prediction for polymer composites and application-oriented GUI development.
CFD modeling, reactor design, data analysis, and process optimization. CFD modeling, reactor design, data analysis, and process optimization.
Translate an operational or product-development challenge into a measurable engineering objective. 운전 현장이나 제품 개발 과정에서의 문제를 공학적으로 정의하고, 이를 해결하기 위한 연구를 수행합니다.
Build reliable modeling pipeline suited to the quality and scale of available data. 확보된 데이터에 적합한 신뢰할 수 있는 모델링 방법론을 개발하고, 자동화를 위한 파이프라인을 구축합니다.
Connect prediction to interpretation, optimization, and deployable engineering workflows. 예측 결과를 기반으로 데이터 해석, 최적화, 그리고 실제 현장 적용까지 한번에 가능할 수 있도록 하나의 workflow로 연결합니다.
