A hybrid MILP-NLP-complementarity decomposition solved via spatial/temporal ADMM yields up to 13x speedup on unbalanced AC power flow-constrained DES design for networks with 55 loads, with maximum 0.61% optimality gap.
Confidence region estimation techniques for nonlinear regression in ground- water flow: Three case studies
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PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.
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Distributed Energy System Design including Unbalanced AC Power Flow for Large LV Networks with ADMM
A hybrid MILP-NLP-complementarity decomposition solved via spatial/temporal ADMM yields up to 13x speedup on unbalanced AC power flow-constrained DES design for networks with 55 loads, with maximum 0.61% optimality gap.
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PISP: Projected-Space Inference of Stellar Parameters
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.