Uncertainty Quantification and Data-Driven Modeling
Uncertainty Quantification and Data-Driven Modeling, Computational Materials Modeling, Virginia Tech
Uncertainty quantification uses statistical principles to quantify the probability of a designed part successfully functioning during its anticipated life span, and/or the accuracy of test results. Data-driven modeling tries to learn from available data instead of traditional modeling with hard-coded instructions.
Faculty
Danesh Tafti, Computational Materials Modeling, Virginia Tech
Danesh Tafti
My research focus is computational fluid dynamics and heat transfer in turbulent multiphase, multicomponent systems. I develop methods and apply them to engineered and natural systems. These methods can be applied to material processing which involve a liquid state or solids mixed in liquids.
Hongliang Xin, Computational Materials Modeling, Virginia Tech
Hongliang Xin
Our group focuses on the development of a multiscale modeling framework that integrates our expertise in ab-initio calculations, kinetic simulations, and statistical learning for energy and electronics applications.
Julianne Chung, Computational Materials Modeling, Virginia Tech
Julianne Chung
The aim of my research is to advance knowledge in the field of computational inverse problems, where the main challenges include ill-posedness of the problem, large parameter dimensions, model inaccuracies, and regularization parameter selection. My research combines rigorous analysis with robust methodologies to address these and other computational challenges.
Kevin Wang, Computational Materials Modeling, Virginia Tech
Kevin Wang
The Multiphysics Modeling and Computation (M2C) Lab focuses on the development of new models, algorithms, and computer programs for simulating engineering and health-related problems involving multiple physical domains, multiple physical fields, and/or different length and time scales. Our areas of expertise include fluid-solid interaction, shock waves, and multiscale material modeling.
Leanna House, Computational Materials Modeling, Virginia Tech
Leanna House
Data exploration with data visualizations that promote human-data interaction and education in Statistics; Bayesian hierarchical modeling with an emphasis in model averaging, dimension reduction, and Bayes linear; Uncertainty quantification of computer models; Statistical applications in education, health analytics, bioinformatics, climatology, hydrology, transportation.
Matthias Chung, Computational Materials Modeling, Virginia Tech
Matthias Chung
My research lies in the interdisciplinary field of computational inverse problems, where the main goal is to estimate underlying model parameters or internal structures from observed measurements (biology, engineering, imaging). The main challenges toward obtaining meaningful real-time solutions are ill-posedness, large parameter dimensions, complex model constraints, and providing uncertainty estimates.
Pinar Acar, Computational Materials Modeling, Virginia Tech
Pinar Acar
We work on multi-scale computational methodologies to achieve a comprehensive understanding of processing, microstructure, and material properties. The main research topics are Integrated Computational Materials Engineering (ICME), multi-scale modeling, optimization, uncertainty quantification, reduced order modeling, and machine learning to study material behavior at length scales ranging from microstructure to component.
Ryan Pollyea, Computational Materials Modeling, Virginia Tech
Ryan Pollyea
The Computational Geofluids Lab is led by Ryan M. Pollyea in the Department of Geosciences. Our student-scholars pursue research at the intersection of geologic fluid systems and energy resources, including geologic CO2 sequestration, geothermal energy systems, and injection-induced earthquakes.