Research Overview
We develop models with enhanced predictive performance by combining uncertainty quantification and machine learning, aiming to create optimized processes.
SEEDS
Uncertainty Quantification and Process Optimization
- Researcher
- KAWAJIRI Yoshiaki
- Keyword
- OptimizationMachine LearningBayesian InferenceUncertainty Quantification
- Contact
-
Related Seeds
- Creation of Innovative Energy and Environmental Materials/Devices in Collaboration with Data Science
- Data-Driven Materials Design
- Modeling of Material Microstructure Formation and Identification of Material Parameters
- Data-Driven Process Control
- Strength and Property Modeling of Structural Materials
- Next-Generation Material Simulations and Analyses Based on Molecular Theory of Complex Matter Systems