Research team

Research staff

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Patrick Blonigan

Senior Member of Technical Staff

Sensitivity analysis, Model reduction, Chaotic dynamical systems, Computational fluid dynamics

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Brian Freno

Senior Member of Technical Staff

Model reduction, Machine learning, Error modeling, Finite element analysis

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Chi Hoang

Senior Computer Science R&D Researcher

Model reduction, Reduced basis method, Domain decomposition, Finite element analysis

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Francesco Rizzi

Senior Computational Scientist

High performacne computing, Uncertainty quantification, Model reduction, Numerical methods, Object-oriented programming

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John Tencer

Principal Member of Technical Staff

Radiation heat transfer, Uncertainty quantification, Model reduction, Finite element analysis

Postdoctoral researchers

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Kookjin Lee

Postdoctoral researcher

Deep learning, Autoencoders, Model reduction, Uncertainty quantification, Sparse linear solvers

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Eric Parish

John von Neumann Postdoctoral Fellow

Model reduction, Closure modeling, Machine learning, Dynamics learning

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Yukiko Shimizu

Postdoctoral researcher

Model reduction, Sensitivity analysis, Chaotic dynamical systems, Numerical methods

Graduate student interns and collaborators

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Ricardo Baptista

PhD Candidate, MIT

Uncertainty quantification, High-dimensional statistics, Machine learning

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Philip Etter

PhD Candidate, Stanford University

Model reduction, Adaptive refinement, Machine learning, Deep learning

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Sofia Guzzetti

PhD candidate, Emory University

Uncertainty quantification, Domain decomposition, Hemodynamics

News

Data-driven coarse propagation to accelerate convergence

Excited to welcome Yuki to our lab

Using regression to model approximate-solution errors

Becoming Associate Editor

in the areas of reduced-order modeling, scientific machine learning, high-performance computing, and uncertainty quantification

Recent Publications

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the …

This work proposes a data-driven method for enabling the efficient, stable time-parallel numerical solution of systems of ordinary …

This work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to …

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions …

This work proposes a technique for constructing a statistical closure model for reduced-order models (ROMs) applied to stationary …

Teaching

Instructor

Teaching assistant

Contact

  • 925 667 1834
  • 7011 East Avenue, MS 9159, Livermore, California, 94550, USA