Ignas Satkauskas is a postdoctoral researcher in the Complex Systems Simulation and Optimization Group in NREL's Computational Science Center. At NREL, Ignas takes part in various projects including operations of electrical grids under uncertainty, optimization of turbine design, coupling of computational fluid dynamics algorithms, modeling of hybrid energy systems, and design and analysis of synthetic power grids with large renewable energy penetrations.
Ignas has a strong background in numerical analysis, probability, statistics. His interests include stochastic and deterministic optimization, uncertainty quantification, complex network theory, data science, machine learning, visualization, and high-performance computing. He received his doctorate in Applied Mathematics from University of Colorado where he developed advanced numerical calculus of probability density functions.
For additional information, see Ignas Satkauskas's LinkedIn profile.
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Renewable energy and electric grid operations
Stochastic and deterministic optimization
Accurate approximation methods and multiresolution
Complex network theory
Data analytics and machine learning
Computational fluid dynamics
Ph.D., Applied Mathematics, University of Colorado
M.S., Applied Mathematics, University of Colorado
B.S., Mathematics, University of Colorado
Postdoctoral Researcher, NREL (2018–present)
Research/Teaching Assistant, University of Colorado (2012–2017)
Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch, 2020 IEEE PES General Meeting (2020)
Scalable Transmission Expansion Under Uncertainty Using Three-stage Stochastic Optimization, IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (2020)
Wind Turbine Rotor Design Optimization Using Importance Sampling, AIAA SciTech Forum (2020)
Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation, AIAA SciTech Forum (2019)
On Computing Distributions of Products of Non-Negative Independent Random Variables, Applied and Computational Harmonic Analysis (2019)
On Computing Distributions of Products of Random Variables via Gaussian Multiresolution Analysis, Applied and Computational Harmonic Analysis (2019)
Nesting an Incompressible-Flow Code within a Compressible-Flow Code: A Two-Dimensional Study, Computers & Fluids (2015)