arXiv subjects

Michael Czekanski

Publications and source records attributed to Michael Czekanski.

5 recordsLinked to original sources

arXiv2026

Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction

Modern stellarators are typically designed by optimizing the shape of the plasma boundary surface, with the parameters taken to be Fourier amplitudes. Many promising optimization algorithms such as Bayesian methods require bound constraints on the parameters and are most efficient when each parameter is scaled similarly to the others. With the typical Fourier parameterization, it is unclear how to set these bounds: wide constraints lead to self-intersecting boundaries and frequent failures of the MHD equilibrium calculation, while tight bound constraints limit expressiveness. To address these issues, here we propose two new parameter spaces for stellarator optimization. Both begin with a dataset of existing stellarator boundaries. In the first approach, a quantile transformation is applied to each Fourier degree of freedom, mapping the data distribution to a uniform distribution on the unit interval. In the second approach, principal component analysis (PCA) is applied to points on the boundaries, followed by a quantile transformation. For both approaches, the transformed variables become the degrees of freedom, naturally bounded to [0, 1]. The PCA method has the additional benefit of dimensionality reduction, with high expressiveness for a small number of parameters. The methods are demonstrated via Bayesian optimization for good alpha-particle confinement with guiding-center tracing inside the optimization loop, using asynchronous parallelization. These optimizations yield stellarator configurations with excellent fast-particle confinement in fields that can be far from quasisymmetric or quasi-isodynamic.

physics.plasm-ph↗

arXiv2026

FIRM3D: Fast ion reduced models in 3D

The dynamics of energetic particle (EP) species, born from fusion reactions or plasma heating schemes, are critical for predicting the behavior of magnetic confinement fusion experiments and future fusion reactors. Because energetic particles are largely collisionless, the orbits of Monte Carlo samples drawn from a given distribution function can be efficiently integrated in prescribed electromagnetic fields. In addition to the static magneto-hydrodynamic (MHD) equilibrium fields produced by the electromagnetic coils of a fusion device, MHD waves can be excited by -- and resonantly transport -- energetic particle populations. FIRM3D is an open-source Python/C++/CUDA software suite for modeling energetic particle dynamics in 3D magnetic fields, available at https://github.com/ColumbiaStellaratorTheory/firm3d. The core guiding-center integration routines grew out of SIMSOPT (Landreman et al., 2021), but have been extended to include additional physics and diagnostics not typically required in the stellarator optimization context. This standalone framework enables focused development of energetic particle physics capabilities with minimal dependencies, making it accessible to the broader stellarator and plasma physics community. Components of FIRM3D include interfaces with MHD equilibrium and wave stability software (BOOZ_XFORM, AE3D, FAR3D); CPU and GPU parallelized integration of the guiding center orbit equation, with symplectic and Runge-Kutta integrator options; and orbit visualization and transport diagnostics, including Poincare maps, orbit classification, and weighted Birkhoff averaging.

physics.plasm-ph↗

arXiv2026

CATAPULT: A CUDA-Accelerated Timestepper for Alpha Particles Using Local Tricubics

We introduce a CUDA-Accelerated Timestepper for Alpha Particles Using Local Tricubics (CATAPULT) for use in Monte Carlo calculations of alpha particle confinement in stellarators. Our GPU implementation is significantly faster than existing parallelized CPU implementations, and handles both equilibrium magnetic fields and Shear Alfven Waves. We test our implementation on several example stellarators to exhibit both the speed and correctness of our code. The source code is included in the firm3d Python package.

physics.comp-ph↗

arXiv2024

Walking on Spheres and Talking to Neighbors: Variance Reduction for Laplace's Equation

Walk on Spheres algorithms leverage properties of Brownian Motion to create Monte Carlo estimates of solutions to a class of elliptic partial differential equations. We propose a new caching strategy which leverages the continuity of paths of Brownian Motion. In the case of Laplace's equation with Dirichlet boundary conditions, our algorithm has improved asymptotic runtime compared to previous approaches. Until recently, estimates were constructed pointwise and did not use the relationship between solutions at nearby points within a domain. Instead, our results are achieved by passing information from a cache of fixed size. We also provide bounds on the performance of our algorithm and demonstrate its performance on example problems of increasing complexity.

physics.comp-ph↗

arXiv2023

Robust and Space-Efficient Dual Adversary Quantum Query Algorithms

The general adversary dual is a powerful tool in quantum computing because it gives a query-optimal bounded-error quantum algorithm for deciding any Boolean function. Unfortunately, the algorithm uses linear qubits in the worst case, and only works if the constraints of the general adversary dual are exactly satisfied. The challenge of improving the algorithm is that it is brittle to arbitrarily small errors since it relies on a reflection over a span of vectors. We overcome this challenge and build a robust dual adversary algorithm that can handle approximately satisfied constraints. As one application of our robust algorithm, we prove that for any Boolean function with polynomially many 1-valued inputs (or in fact a slightly weaker condition) there is a query-optimal algorithm that uses logarithmic qubits. As another application, we prove that numerically derived, approximate solutions to the general adversary dual give a bounded-error quantum algorithm under certain conditions. Further, we show that these conditions empirically hold with reasonable iterations for Boolean functions with small domains. We also develop several tools that may be of independent interest, including a robust approximate spectral gap lemma, a method to compress a general adversary dual solution using the Johnson-Lindenstrauss lemma, and open-source code to find solutions to the general adversary dual.

quant-ph↗