Stephen Kasdorf
Ph.D. FinalOct 16, 2024, 10:00 am - 11:00 am
LSC 384
FULL-WAVE AND ASYMPTOTIC COMPUTATIONAL ELECTROMAGNETICS METHODS: ON THEIR USE AND IMPLEMENTATION IN RECEIVED SIGNAL STRENGH, RADAR-CROSS-SECTION, AND UNCERTAINTY QUANTIFICATION PREDICTIONS
Abstract: We propose and evaluate several improvements to the accuracy of the shooting and bouncing
rays (SBR) method for ray-tracing (RT) electromagnetic modeling. A per-ray cone angle
calculation is introduced, with the maximum separation angle determined for each individual ray
based on local neighbors, allowing the smallest theoretical error in SBR. This enables adaptive
ray spawning and provides a unique analysis of the effect of ray cone sizes on accuracy. For
conventional uniform angular distribution, we derive an optimal cone angle to further enhance
accuracy. Both approaches are integrated with icosahedral ray spawning geometry and a double-
counted ray removal technique, which avoids complex ray path searches. The results
demonstrate that the advanced SBR method can perform wireless propagation modeling of
tunnel environments with accuracy comparable to the image theory RT method, but with much
greater efficiency.
To further advance the efficiency of the SBR method, we propose a unified parallelization
framework leveraging NVIDIA OptiX Prime programming interfaces on graphics processing
units (GPUs). The framework achieves comprehensive parallelization of all components of the
SBR algorithm, including traditionally sequential tasks like electric field computation and
postprocessing. Through optimization of memory usage and GPU resources, the new SBR
method achieves upwards of 99% parallelism under Amdahl’s scaling law. This innovative parallelization yields dramatic speedups without sacrificing the previously enhanced accuracy of
the SBR method, demonstrating an unparalleled level of computational efficiency for large-scale
electromagnetic propagation simulations.
Finally, we implement and validate several advanced Kriging methodologies for uncertainty
quantification (UQ) in computational electromagnetics (CEM). The universal Kriging, Taylor
Kriging, and gradient-enhanced Kriging methods are applied to reconstruct probability density
functions, offering efficient alternatives to Monte Carlo simulations. We further propose the
novel gradient-enhanced Taylor Kriging (GETK) method, which combines the advantages of
gradient information and basis functions, yielding superior surrogate function accuracy and faster
convergence. Numerical results using higher-order finite-element scattering modeling show that
GETK dramatically outperforms other Kriging and non-Kriging methods in UQ problems,
accurately predicting the impact of stochastic input parameters, such as material uncertainties, on
quantities of interest like radar cross-section.
rays (SBR) method for ray-tracing (RT) electromagnetic modeling. A per-ray cone angle
calculation is introduced, with the maximum separation angle determined for each individual ray
based on local neighbors, allowing the smallest theoretical error in SBR. This enables adaptive
ray spawning and provides a unique analysis of the effect of ray cone sizes on accuracy. For
conventional uniform angular distribution, we derive an optimal cone angle to further enhance
accuracy. Both approaches are integrated with icosahedral ray spawning geometry and a double-
counted ray removal technique, which avoids complex ray path searches. The results
demonstrate that the advanced SBR method can perform wireless propagation modeling of
tunnel environments with accuracy comparable to the image theory RT method, but with much
greater efficiency.
To further advance the efficiency of the SBR method, we propose a unified parallelization
framework leveraging NVIDIA OptiX Prime programming interfaces on graphics processing
units (GPUs). The framework achieves comprehensive parallelization of all components of the
SBR algorithm, including traditionally sequential tasks like electric field computation and
postprocessing. Through optimization of memory usage and GPU resources, the new SBR
method achieves upwards of 99% parallelism under Amdahl’s scaling law. This innovative parallelization yields dramatic speedups without sacrificing the previously enhanced accuracy of
the SBR method, demonstrating an unparalleled level of computational efficiency for large-scale
electromagnetic propagation simulations.
Finally, we implement and validate several advanced Kriging methodologies for uncertainty
quantification (UQ) in computational electromagnetics (CEM). The universal Kriging, Taylor
Kriging, and gradient-enhanced Kriging methods are applied to reconstruct probability density
functions, offering efficient alternatives to Monte Carlo simulations. We further propose the
novel gradient-enhanced Taylor Kriging (GETK) method, which combines the advantages of
gradient information and basis functions, yielding superior surrogate function accuracy and faster
convergence. Numerical results using higher-order finite-element scattering modeling show that
GETK dramatically outperforms other Kriging and non-Kriging methods in UQ problems,
accurately predicting the impact of stochastic input parameters, such as material uncertainties, on
quantities of interest like radar cross-section.
Adviser: Branislav Notaros
Co-Adviser: NA
Non-ECE Member: Karan Venayagamoorthy
Member 3: Jesse Wilson
Addional Members: Milan Ilic
Co-Adviser: NA
Non-ECE Member: Karan Venayagamoorthy
Member 3: Jesse Wilson
Addional Members: Milan Ilic
Publications:
S. Kasdorf, J. J. Harmon and B. M. Notaroš, "Kriging Methodology for Uncertainty
Quantification in Computational Electromagnetics," in IEEE Open Journal of Antennas
and Propagation, vol. 5, no. 2, pp. 474-486, April 2024
S. Kasdorf, B. Troksa, C. Key, J. Harmon, S. Pasricha and B. M. Notaroš, "Parallel GPU
Optimization of the Shooting and Bouncing Ray Tracing Methodology for Propagation
Modeling," in IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 174-
182, Jan. 2024
S. Kasdorf, B. Troksa, C. Key, J. Harmon and B. M. Notaroš, "Advancing Accuracy of
Shooting and Bouncing Rays Method for Ray-Tracing Propagation Modeling Based on
Novel Approaches to Ray Cone Angle Calculation," in IEEE Transactions on Antennas
and Propagation, vol. 69, no. 8, pp. 4808-4815, Aug. 2021
C. Key, B. A. Troksa, S. Kasdorf and B. M. Notaroš, "Non-Self-Adjacent Ray Classes for
Parallelizable Shooting–Bouncing Ray Tracing Double Count Removal," in IEEE
Journal on Multiscale and Multiphysics Computational Techniques, vol. 5, pp. 245-254,
2020
S. Kasdorf, J. J. Harmon and B. M. Notaroš, "Kriging Methodology for Uncertainty
Quantification in Computational Electromagnetics," in IEEE Open Journal of Antennas
and Propagation, vol. 5, no. 2, pp. 474-486, April 2024
S. Kasdorf, B. Troksa, C. Key, J. Harmon, S. Pasricha and B. M. Notaroš, "Parallel GPU
Optimization of the Shooting and Bouncing Ray Tracing Methodology for Propagation
Modeling," in IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 174-
182, Jan. 2024
S. Kasdorf, B. Troksa, C. Key, J. Harmon and B. M. Notaroš, "Advancing Accuracy of
Shooting and Bouncing Rays Method for Ray-Tracing Propagation Modeling Based on
Novel Approaches to Ray Cone Angle Calculation," in IEEE Transactions on Antennas
and Propagation, vol. 69, no. 8, pp. 4808-4815, Aug. 2021
C. Key, B. A. Troksa, S. Kasdorf and B. M. Notaroš, "Non-Self-Adjacent Ray Classes for
Parallelizable Shooting–Bouncing Ray Tracing Double Count Removal," in IEEE
Journal on Multiscale and Multiphysics Computational Techniques, vol. 5, pp. 245-254,
2020
Program of Study:
ECE 444
ECE 504
ECE 541
ECE 604
ENGR 550
MATH 510
MATH 520
MATH 535
ECE 444
ECE 504
ECE 541
ECE 604
ENGR 550
MATH 510
MATH 520
MATH 535