From the 2 of 19 papers with an AI index.
86 citations
- Peking UniversityCN8 papers
- Massachusetts Institute of TechnologyUS7 papers
- Monash UniversityAU7 papers
- Tsinghua UniversityCN7 papers
- University of California SystemUS7 papers
- University of Milano-BicoccaIT7 papers
- University of Naples Federico IIIT7 papers
- University of ZurichCH7 papers
- California Institute of TechnologyUS6 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Cornell UniversityUS6 papers
- Eötvös Loránd UniversityHU6 papers
8 papers
Process Optimization Under Uncertainty for Improving the Bond Quality of Polymer Filaments in Fused Filament Fabrication
Berkcan Kapusuzoglu, Matthew Sato, Sankaran Mahadevan +1
This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabric…
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
Berkcan Kapusuzoglu, Sankaran Mahadevan, Shunsaku Matsumoto +2
This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for ti…
Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
Berkcan Kapusuzoglu, Paromita Nath, Matthew Sato +2
This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed…
Adaptive surrogate modeling for high-dimensional spatio-temporal output
Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi +2
This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is…
Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data
Berkcan Kapusuzoglu, Sankaran Mahadevan
When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the accuracy a…
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
Berkcan Kapusuzoglu, Sankaran Mahadevan
This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for pr…