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I am interested in building optimization frameworks and reduced-order models for complex engineering systems. My focus is on large-scale, physics-based simulations and sparse parameter identification. Passionate about delivering computationally efficient solutions that enhance model interpretability and performance.
Research Highlights
Researched and applied advanced optimization methods to extract interpretable parameters from noisy and incomplete data.
Developed and implemented sparse Hessian solvers in JAX to improve computational efficiency for large-scale problems.
Reduced Order Models for Circuits
Reduced Order Models for Circuits
Investigated reduced-order models for electrical circuits where state measurements are not directly attainable.
Applied advanced optimization algorithms to calibrate model parameters, ensuring accurate representation of circuit dynamics.
Physics Informed Neural Networks
Physics Informed Neural Networks
Engineered neural network architectures tailored to solving incompressible Stokes equations under complex boundary conditions.
Analyzed the impact of elliptic regularity theory on training stability and solution accuracy for PINNs.
Implemented efficient numerical methods to solve the Helmholtz Paraxial wave equation.
Compared and analyzed the computational complexity of spectral versus sinc-based approaches, optimizing for performance.
Publications (*in review)
T. Meissner and K. Glasner. Simultaneous model discovery and state estimation under high data corruption. Accepted, SIAM Journal on Applied Dynamical Systems (SIADS), 2026. Preprint: arXiv.2406.06707
T. Meissner, E. Huynh, P. Kuberry and P. Bochev. A deep least-squares method for the Stokes equations. Computers & Mathematics with Applications (CAMWA), 2025.
T. Meissner, B. Paskaleva, P. Bochev. A sparse state-space compact modeling approach for electrical circuits. Accepted 2025, International Conference on Large-Scale Scientific Computations (LSSC 2025)
*S. Hassan, J. Wang, T. Meissner, P. Deymier, M. Latypov. Eutectic and peritectic equilibria in coherent binary alloys. In review. Preprint: arXiv.2602.15251.
*T. Meissner, J. Hanson, J. Young, P. Bochev. Compact circuit models for EM effects based on statistical decomposition of system dynamics. In review.
*T. Meissner, B. Paskaleva, P. Bochev. Sparse state-space models for circuits under ionizing radiation. In review.
Presentations
A Sparse State-Space Model for Electrical Circuits. Digital Twins in Engineering & Artificial Intelligence and Computational Methods in Applied Science (DTE & AICOMAS), February 2025, Paris, France.
Sparse state-space models for circuits under ionizing radiation. Hardened Electronics and Radiation Technology (HEART) Conference, April 2025, Monterey, CA.
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