arXiv subjects

Paul Fischer

Publications and source records attributed to Paul Fischer.

At least 19 recordsLinked to original sources

arXiv2026

Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors

High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the initial flow-development period required to reach statistically stationary conditions. This work investigates the use of a Super-Resolution Graph Neural Network (SR-GNN) to improve the initialization of high-order NekRS simulations. Lower-order P = 2 velocity fields are used as inputs to reconstruct higher-order representations, which are then used as initial conditions for P = 7 restart simulations. The approach is evaluated using a 146-pebble bed at Re = 1000, Re = 2500, and Re = 5000, with pressure-drop convergence used as the main figure of merit. The SR-GNN models were trained using paired low- and high-order snapshots and were first evaluated through qualitative inference comparisons. High-order restart simulations showed that, for Re = 1000 and Re = 2500, the SR-GNN initialized cases produced pressure-drop histories similar to direct restarts from true P = 2 fields. For Re = 5000, however, the super-resolved field restart approached the statistically stationary P = 7 pressure-drop range faster than both the direct P = 2 restart and the reference P = 7 simulation initialized from a uniform velocity field. The trained Re = 5000 model was also applied to a larger 1568-pebble bed, demonstrating qualitative applicability of the workflow to a significantly larger packed-bed geometry. These results indicate that SR-GNN-based initialization is a promising strategy for reducing high-order flow-development cost, while also motivating further work on broader Reynolds-number and geometry generalization.

physics.flu-dyn↗

arXiv2026

VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation

Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data. A common case is pediatric care, where models trained on adult cohorts can silently under-perform on children with no indication that something has gone wrong. As retraining with labeled pediatric data is often infeasible, detecting such failures at inference time is a critical clinical need. Building on the VIDS (Variational Inference under Distribution Shifts) framework, we introduce VIDS-Seg, which applies amortized variational inference over a lightweight prediction head to make this adaptive, OOD-aware prior tractable for dense image segmentation. We evaluate VIDS-Seg on left ventricular segmentation in echocardiography, a setting where pediatric anatomy differs systematically from the adult population most segmentation models are trained on, training on an adult cohort (EchoNet-Dynamic) and evaluating zero-shot on a pediatric cohort (EchoNet-Pediatric). Across all age strata, VIDS-Seg matches competitive baselines in segmentation accuracy while producing substantially higher spatial correspondence between predicted uncertainty and segmentation error, an advantage that persists even after applying temperature scaling to all baselines. Downstream, it yields more accurate and stable ejection fraction estimates and more reliable detection of cardiac malfunction in the infant subgroup. Our results indicate that OOD-aware uncertainty quantification can serve as a practical safety layer for deployed segmentation models, enabling detection of silent failures in underrepresented subgroups without retraining or additional labeled data.

cs.CV↗

arXiv2026

Coarse Solvers for Exascale Solution of Poisson Problems

We present a two-level Schwarz method as an alternative to Algebraic Multigrid method(AMG) used as the last level (coarse) solver of the p-multigrid pMG preconditioner for pressure Poisson equation resulting from Spectral/Finite element descretization of incompressible Navier-Stokes equation. Proposed Schwarz method consits of a local problem in the original pMG coarse space and a global coarse problem. Main contribution of the paper is a novel, structured and a non-nested coarse space for the global coarse problem. Structured nature of the proposed global coarse space enable communication-free interpolation between the original p-multgrid coarse space and the global coarse problem. We demonstrate the effectiveness of the proposed method compared to the state of the art AMG solver BoomerAMG by a series of experiments performed using Nek5000/RS, a suite of highly scalable incompressible Navier-Stokes solvers, on Summit/Frontier supercomputers at Oak Ridge Leadership Computing Facility.

math.NA↗

arXiv2026

parRSB: Exascale Spectral Element Mesh Partitioning

We introduce parRSB - a parallel, highly scalable graph partitioner for spectral element meshes that produce high quality partitions. parRSB is based on Recursive Spectral Bisection (RSB) algorithm implemented on the dual graph of the input mesh. RSB uses the Fiedler vector, which is the eigenvector associated with the smallest non-zero eigenvalue of the Laplacian matrix of the dual graph for making partitioning decisions and tries to minimize the communication volume between the partitions. We implemented two numerical methods: Lanczos, and Inverse iteration using Conjugate Gradient method to compute the Fiedler vector. We present partitioning results using parRSB on Summit and Frontier supercomputers at Oak Ridge National Laboratory to illustrate the quality of the partitions produced by parRSB and the scalability of our implementation. We also present results for some of the optimizations we did to speed up the partitioning process.

cs.DC↗

arXiv2025

Multiply charged uranium monoxide as a versatile probe of fundamental physics

Multiply charged actinide molecules provide a unique platform to study fundamental physics and the chemical bond under extreme conditions. Beyond the inherently large relativistic effects associated with a high proton number $Z$, an increased molecular charge can further enhance the electronic sensitivity to symmetry-violating nuclear effects, including nuclear Schiff moments. Experimental investigations of multiply charged actinide molecules are challenging because the high charges severely destabilize chemical bonds, leading to spontaneous Coulomb explosion. We demonstrate a method to systematically generate and detect molecular ions at the edge of chemical stability. By applying high-fluence laser ablation to a depleted uranium metal foil, we produce atomic uranium ions U$^{z+}$ and uranium monoxide cations UO$^{z+}$ with $z = 1$--4. Among them, we observe UO$^{3+}$ and UO$^{4+}$, which exhibit comparatively simple electronic structures and are therefore promising for precision spectroscopy. The experiments are supported by relativistic density functional theory calculations of equilibrium bond lengths, charge distributions, and binding energies of all observed molecules. Calculations of symmetry-violating properties suggest a pronounced sensitivity of UO$^{3+}$ to hadronic $CP$ violation. This approach opens a pathway for high-precision investigations of fundamental symmetries and the exploration of relativistic actinide chemistry in previously inaccessible regimes.

physics.chem-ph↗

arXiv2025

CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRI

Magnetic Resonance Imaging (MRI) offers unparalleled soft-tissue contrast but is fundamentally limited by long acquisition times. While deep learning-based accelerated MRI can dramatically shorten scan times, the reconstruction from undersampled data introduces ambiguity resulting from an ill-posed problem with infinitely many possible solutions that propagates to downstream clinical tasks. This uncertainty is usually ignored during the acquisition process as acceleration factors are often fixed a priori, resulting in scans that are either unnecessarily long or of insufficient quality for a given clinical endpoint. This work introduces a dynamic, uncertainty-aware acquisition framework that adjusts scan time on a per-subject basis. Our method leverages a probabilistic reconstruction model to estimate image uncertainty, which is then propagated through a full analysis pipeline to a quantitative metric of interest (e.g., patellar cartilage volume or cardiac ejection fraction). We use conformal prediction to transform this uncertainty into a rigorous, calibrated confidence interval for the metric. During acquisition, the system iteratively samples k-space, updates the reconstruction, and evaluates the confidence interval. The scan terminates automatically once the uncertainty meets a user-predefined precision target. We validate our framework on both knee and cardiac MRI datasets. Our results demonstrate that this adaptive approach reduces scan times compared to fixed protocols while providing formal statistical guarantees on the precision of the final image. This framework moves beyond fixed acceleration factors, enabling patient-specific acquisitions that balance scan efficiency with diagnostic confidence, a critical step towards personalized and resource-efficient MRI.

eess.IV↗

arXiv2025

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning

The Lattice Boltzmann method (LBM) offers a powerful and versatile approach to simulating diverse hydrodynamic phenomena, spanning microfluidics to aerodynamics. The vast range of spatiotemporal scales inherent in these systems currently renders full resolution impractical, necessitating the development of effective closure models for under-resolved simulations. Under-resolved LBMs are unstable, and while there is a number of important efforts to stabilize them, they often face limitations in generalizing across scales and physical systems. We present a novel, data-driven, multiagent reinforcement learning (MARL) approach that drastically improves stability and accuracy of coarse-grained LBM simulations. The proposed method uses a convolutional neural network to dynamically control the local relaxation parameter for the LB across the simulation grid. The LB-MARL framework is showcased in turbulent Kolmogorov flows. We find that the MARL closures stabilize the simulations and recover the energy spectra of significantly more expensive fully resolved simulations while maintaining computational efficiency. The learned closure model can be transferred to flow scenarios unseen during training and has improved robustness and spectral accuracy compared to traditional LBM models. We believe that MARL closures open new frontiers for efficient and accurate simulations of a multitude of complex problems not accessible to present-day LB methods alone.

physics.flu-dyn↗

arXiv2025

The PUMA offline ion source beamline

The antiProton Unstable Matter Annihilation experiment (PUMA) at CERN aims to study the nucleonic composition in the matter density tail of stable and radioactive nuclei using low-energy antiprotons. Since there is no facility in which both low-energy antiprotons and radioactive nuclei can be produced, the experimental realization with exotic nuclei requires the transportation of the antiprotons from the Extra Low ENergy Antiproton (ELENA) facility to the nearby located Isotope mass Separator On-Line DEvice (ISOLDE). For tests and first applications of the proposed experimental technique to stable isotopes at ELENA, a dedicated offline ion source beamline was developed that will provide isotopically pure, cooled and bunched ion beams with intensities of more than $10^4$ ions per bunch while maintaining a vacuum of better than $5\times 10^{-10}$ mbar at the handover point. This offline ion source beamline is characterized and its capabilities are demonstrated using the example of stable krypton isotopes.

physics.acc-ph↗

arXiv2025

Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow

Turbulent heat and momentum transfer processes due to thermal convection cover many scales and are of great importance for several natural and technical flows. One consequence is that a fully resolved three-dimensional analysis of these turbulent transfers at high Rayleigh numbers, which includes the boundary layers, is possible only using supercomputers. The visualization of these dynamics poses an additional hurdle since the thermal and viscous boundary layers in thermal convection fluctuate strongly. In order to track these fluctuations continuously, data must be tapped at high frequency for visualization, which is difficult to achieve using conventional methods. This paper makes two main contributions in this context. First, it discusses the simulations of turbulent Rayleigh-B\'enard convection up to Rayleigh numbers of $Ra=10^{12}$ computed with NekRS on GPUs. The largest simulation was run on 840 nodes with 3360 GPU on the JUWELS Booster supercomputer. Secondly, an in-situ workflow using ASCENT is presented, which was successfully used to visualize the high-frequency turbulent fluctuations.

physics.flu-dyn↗

arXiv2025

General Field Evaluation in High-Order Meshes on GPUs

Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for high-order unstructured meshes partitioned in parallel with MPI, as it requires identifying the element that overlaps a given point and computing the corresponding reference space coordinates. We present a robust and efficient technique for general field evaluation in large-scale high-order meshes with quadrilaterals and hexahedra. In the proposed method, a combination of globally partitioned and processor-local maps are used to first determine a list of candidate MPI ranks, and then locally candidate elements that could contain a given point. Next, element-wise bounding boxes further reduce the list of candidate elements. Finally, Newton's method with trust region is used to determine the overlapping element and corresponding reference space coordinates. Since GPU-based architectures have become popular for accelerating computational analyses using meshes with tensor-product elements, specialized kernels have been developed to utilize the proposed methodology on GPUs. The method is also extended to enable general field evaluation on surface meshes. The paper concludes by demonstrating the use of proposed method in various applications ranging from mesh-to-mesh transfer during r-adaptivity to Lagrangian particle tracking.

cs.MS↗

arXiv2024

DDES Study of Confined and Unconfined NACA Wing Sections Using Spectral Elements

We develop hybrid RANS-LES strategies within the spectral element code Nek5000 based on the $k-\tau$ class of turbulence models. We chose airfoil sections at small flight configurations as our target problem to comprehensively test the solver accuracy and performance. We present verification and validation results of an unconfined NACA0012 wing section in a pure RANS and in a hybrid RANS-LES setup for an angle of attack ranging from 0 to 90 degrees. The RANS results shows good corroboration with existing experimental and numerical datasets for low incoming flow angles. A small discrepancy appears at higher angle in comparison with the experiments, which is in line with our expectations from a RANS formulation. On the other hand, DDES captures both the attached and separated flow dynamics well when compared with available numerical datasets. We demonstrate that for the hybrid turbulence modeling approach a high-order spectral element discretization converges faster (i.e., with less resolution) and captures the flow dynamics more accurately than representative low-order finite-volume and finite-difference approaches. We also revise some of the guidelines on sample size requirements for statistics convergence. Furthermore, we analyze some of the observed discrepancies of our unconfined DDES at higher angles with the experiments by evaluating the side wall "blocking" effect. We carry out additional simulations in a confined 'numerical wind tunnel' and assess the observed differences as a function of Reynolds number.

cs.CE↗

arXiv2024

Modeling Turbulence in the Atmospheric Boundary Layer with Spectral Element and Finite Volume Methods

We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this paper, we study a GABLS benchmark problem using high-order spectral element code Nek5000/RS and a block-structured second-order finite-volume code AMR-Wind which are supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) and ExaWind projects, respectively, targeting application simulations on various acceleration-device based exascale computing platforms. As for Nek5000/RS we demonstrate our newly developed subgrid-scale (SGS) models based on mean-field eddy viscosity (MFEV), high-pass filter (HPF), and Smagorinsky (SMG) with traction boundary conditions. For the traction boundary conditions, a novel analytical approach is presented that solves for the surface friction velocity and surface kinematic temperature flux. For AMR-Wind, standard SMG is used and discussed in detail the traction boundary conditions for convergence. We provide low-order statistics, convergence and turbulent structure analysis. Verification and convergence studies were performed for both codes at various resolutions and it was found that Nek5000/RS demonstrate convergence with resolution for all ABL bulk parameters, including boundary layer and low level jet (LLJ) height. Extensive comparisons are presented with simulation data from the literature.

cs.CE↗

arXiv2024

Spectral Element Simulation of Liquid Metal Magnetohydrodynamics

A spectral-element-based formulation of incompressible MHD is presented in the context of the open-source fluid-thermal code, Nek5000/RS. The formulation supports magnetic fields in a solid domain that surrounds the fluid domain. Several steady-state and time-transient model problems are presented as part of the code verification process. Nek5000/RS is designed for large-scale turbulence simulations, which will be the next step with this new MHD capability.

cs.CE↗

arXiv2024

Exascale Simulations of Fusion and Fission Systems

We discuss pioneering heat and fluid flow simulations of fusion and fission energy systems with NekRS on exascale computing facilities, including Frontier and Aurora. The Argonne-based code, NekRS, is a highly-performant open-source code for the simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on rapidly convergent high-order spectral element discretizations that feature minimal numerical dissipation and dispersion. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Frontier, Nek5000/RS has achieved an unprecedented milestone in breaching over 1 trillion degrees of freedom with the spectral element methods for the simulation of the CHIMERA fusion technology testing platform. We also demonstrate for the first time the use of high-order overset grids at scale.

cs.CE↗

arXiv2024

nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulations

Exascale computing enables high-fidelity simulations of chemically reactive flows in practical geometries and conditions, and paves the way for valuable insights that can optimize combustion processes, ultimately reducing emissions and improving fuel combustion efficiency. However, this requires software that can fully leverage the capabilities of current high performance computing systems. The paper introduces nekCRF, a high-order reactive low Mach flow solver specifically designed for this purpose. Its capabilities and efficiency are showcased on the pre-exascale system JUWELS Booster, a GPU-based supercomputer at the J\"{u}lich Supercomputing Centre including a validation across diverse cases of varying complexity.

physics.comp-ph↗

arXiv2024

Conformal Performance Range Prediction for Segmentation Output Quality Control

Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential for intuitive output quality control. However, such performance estimates rely on calibrated softmax outputs, which is often not the case in modern neural networks. Moreover, the estimates do not take into account inherent uncertainty in segmentation tasks. These limitations may render precise performance predictions unattainable, restricting the practical applicability of performance estimation methods. To address these challenges, we develop a novel approach for predicting performance ranges with statistical guarantees of containing the ground truth with a user specified probability. Our method leverages sampling-based segmentation uncertainty estimation to derive heuristic performance ranges, and applies split conformal prediction to transform these estimates into rigorous prediction ranges that meet the desired guarantees. We demonstrate our approach on the FIVES retinal vessel segmentation dataset and compare five commonly used sampling-based uncertainty estimation techniques. Our results show that it is possible to achieve the desired coverage with small prediction ranges, highlighting the potential of performance range prediction as a valuable tool for output quality control.

eess.IV↗

arXiv2024

PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration

Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural network-based registration techniques enable fast and accurate registration, the majority of existing approaches are not able to estimate uncertainty. Here, we present PULPo, a method for probabilistic deformable registration capable of uncertainty quantification. PULPo probabilistically models the distribution of deformation fields on different hierarchical levels combining them using Laplacian pyramids. This allows our method to model global as well as local aspects of the deformation field. We evaluate our method on two widely used neuroimaging datasets and find that it achieves high registration performance as well as substantially better calibrated uncertainty quantification compared to the current state-of-the-art.

cs.CV↗

arXiv2024

Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy

Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS.

cs.LG↗