Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success and tradeoffs. However, there ...
Abstract: The implicit Finite-Difference Time-Domain (FDTD) methods represent a class of algorithms that alleviate or even eliminate the Courant–Friedrich-Lev (CFL) stability condition inherent in ...
Abstract: As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms.
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