Author:
Tao Ye,Ferranti Francesco,Nakhla Michel S.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Radiation
Cited by
8 articles.
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1. Uncertainty Quantification in PEEC Method: A Physics-Informed Neural Networks-Based Polynomial Chaos Expansion;2024 IEEE Joint International Symposium on Electromagnetic Compatibility, Signal & Power Integrity: EMC Japan / Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Japan/APEMC Okinawa);2024-05-20
2. Derivative-Enhanced Rational Polynomial Chaos for Uncertainty Quantification;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-04
3. An Adaptive Variable Partitioning Approach to Quantifying High-Dimensional Uncertainties in Frequency Selective Surfaces;IEEE Transactions on Microwave Theory and Techniques;2024
4. Fast Uncertainty Quantification by Sparse Data Learning From Multiphysics Systems;IEEE Transactions on Microwave Theory and Techniques;2023-10
5. An Efficient SSFEM-POD Scheme for Wideband Stochastic Analysis of Permittivity Variations;IEEE Transactions on Antennas and Propagation;2023-02