Our reduced-order modeling project aims to develop a methodology to reduce the computation resources and increase the execution speed of high-performance computing simulations of importance to national security missions. Our choice of radiation transport and hydrodynamics as physical models for reduced-modeling research are relevant to both high-energy-density science and stockpile stewardship science.
Choi, Y. 2018. "ST-GNAT and SNS: Model Order Reduction Techniques for Nonlinear Dynamical Systems." Linear Algbebra and Optimization Seminar, Stanford, CA, Oct. 2018. LLNL-PRES-758124.
Choi, Y. and B. Arrighi. 2018. "Space-Time Reduced Order Model for Dynamical Systems." Data Science Workshop, Livermore, CA, Aug. 2018. LLNL-POST-753977.
Choi, Y. and K. Carlberg. 2018. "Space-Time Least-Squares Petrov-Galerkin Nonlinear Model Reduction." West Coast ROM Workshop, Berkeley, CA, Nov. 2017. LLNL-PRES-728809.
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