TY - GEN A1 - Kharma, Sami A1 - Wies, Tobias A1 - Schintke, Florian T1 - Comprehensive Plugin-Based Monitoring of Nexflow Workflow Executions T2 - SCA/HPC Asia 2026 Y1 - 2026 UR - https://www.sc-asia.org/2026/data/poster/post140.pdf ER - TY - JOUR A1 - Schütte, Christof A1 - Sikorski, Alexander A1 - Kresse, Jakob A1 - Weber, Marcus T1 - On-the-Fly Lifting of Coarse Reaction-Coordinate Paths to Full-Dimensional Transition Path Ensembles N2 - Effective dynamics on a low-dimensional collective-variable (CV) or latent space can be simulated far more cheaply than the underlying high-dimensional stochastic system, but exploiting such coarse predictions requires lifting: turning a coarse CV trajectory into dynamically consistent full-dimensional states and path ensembles, without relying on global sampling of invariant or conditional fiber measures. We present a local, on-the-fly lifting strategy based on guided full-system trajectories. First an effective model in CV space is used to obtain a coarse reference trajectory. Then, an ensemble of full-dimensional trajectories is generated from a guided version of the original dynamics, where the guidance steers the trajectory to track the CV reference path. Because guidance biases the path distribution, we correct it via pathwise Girsanov reweighting, yielding a correct-by-construction importance-sampling approximation of the conditional law of the uncontrolled dynamics. We further connect the approach to stochastic optimal control, clarifying how coarse models can inform variance-reducing guidance for rare-event quantities. Numerical experiments demonstrate that inexpensive coarse transition paths can be converted into realistic full-system transition pathways (including barrier crossings and detours) and can accelerate estimation of transition pathways and statistics while providing minimal bias through weighted ensembles. Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2603.23266 ER - TY - GEN A1 - Ramaki, Niaz Mohammad A1 - Schintke, Florian T1 - Ensuring Reproducibility in Stream Processing with Blockchain Technologies T2 - 2025 11th International Conference on Computer and Communications (ICCC) Y1 - 2025 U6 - https://doi.org/10.1109/ICCC68654.2025.11437772 SP - 1383 EP - 1391 PB - IEEE ER - TY - JOUR A1 - Villani, Paolo A1 - Andrés Arcones, Daniel A1 - Unger, Jörg F. A1 - Weiser, Martin T1 - Gaussian mixture models for model improvement N2 - Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviations of simulation from measurements are ubiquitous even after model calibration due to the model discrepancy, which may result from deliberate modeling decisions, ignorance, or lack of knowledge. If the mismatch between simulation and measurements are deemed unacceptable, the model has to be improved. Targeted model improvement is challenging due to a non-local impact of model discrepancies on measurements and the dependence on sensor configurations. Many approaches to model improvement, such as Bayesian calibration with additive mismatch terms, gray-box models, symbolic regression, or stochastic model updating, often lack interpretability, generalizability, physical consistency, or practical applicability. This paper introduces a non-intrusive approach to model discrepancy analysis using mixture models. Instead of directly modifying the model structure, the method maps sensor readings to clusters of physically meaningful parameters, automatically assigning sensor readings to parameter vector clusters. This mapping can reveal systematic discrepancies and model biases, guiding targeted, physics-based refinements by the modeler. The approach is formulated within a Bayesian framework, enabling the identification of parameter clusters and their assignments via the Expectation-Maximization (EM) algorithm. The methodology is demonstrated through numerical experiments, including an illustrative example and a real-world case study of heat transfer in a concrete bridge. Y1 - 2026 ER - TY - CHAP A1 - Riccardi, Gabor A1 - Lindner, Niels T1 - On The Minimum-Weight Forward (Weakly) Fundamental Cycle Basis Problem in Directed Graphs T2 - International Network Optimization Conference 2026 (INOC 2026) N2 - The cycle space of a directed graph is generated by a cycle basis, where, in general, cycles are allowed to have both forward and backward arcs. In a forward cycle, all arcs have to follow the given direction. We study the existence, structure, and computational complexity of minimum-weight forward cycle bases in directed graphs. We give a complete structural characterization of digraphs that admit weakly fundamental (and hence integral) forward cycle bases, showing that this holds if and only if every block is either strongly connected or a single arc. We further provide an easily verifiable characterization of when a strongly connected digraph admits a forward fundamental cycle basis, proving that such a basis exists if and only if the set of directed cycles has cardinality equal to the cycle rank; in this case, the basis is unique and computable in polynomial time, and nonexistence can likewise be certified efficiently. Lastly, we show that while minimum-weight forward fundamental cycle bases can be found in polynomial time whenever they exist, the minimum-weight forward weakly fundamental cycle basis problem is NP-hard via a polynomial-time reduction from the minimum-weight weakly fundamental cycle basis problem on digraphs with metric weights. Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Hutzler, Merit A1 - Kreowsky, Philipp A1 - De Lucia, Marco A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Spazier, Johannes A1 - Stabernack, Benno A1 - Yahdzhyiev, Serhii T1 - Using FPGA-based Network-Attached Accelerators for Energy-Efficient AI Training in HPC Datacenters T2 - 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) N2 - FPGA-based Network-Attached Accelerators offer a disaggregated alternative to closely-coupled FPGAs or GPUs, but their adoption is very limited due to missing hardware/software frameworks. However, the usability and energy-efficiency of FPGAs for HPC use-cases has been demonstrated previously. Yet, the support by HPC infrastructure for those devices is lacking behind other accelerators. This paper addresses this shortcoming and demonstrates a full-stack approach that seamlessly integrates Network-Attached Accelerators in HPC datacenters and allows flexible and scalable usage of FPGAs. The presented work includes the according framework, integration steps and a show-case application from the geoscience domain. We evaluate our approach by comparing the training of a Physics-Informed Neural Network on the NAA against two GPU implementations. The NAA reduces total energy consumption by about 50% and 17% compared to the Keras and CUDA implementation, respectively. These results demonstrate that FPGA-based NAAs can be successfully integrated into HPC environments and are a viable path toward more energy-efficient AI training. Y1 - 2026 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Jocas, Arturas A1 - Weiser, Martin T1 - An Eikonal Approach for Globally Optimal Free Flight Trajectories N2 - We present an eikonal-based approach that is capable of finding a continuous globally optimal trajectory for an aircraft in a stationary wind field. This minimizes emissions and fuel consumption. If the destination is close to a cut locus of the associated Hamilton-Jacobi-Bellman equation, small numerical discretization errors can lead to selecting a merely locally optimal trajectory and missing the globally optimal one. Based on finite element error estimates, we construct a trust region around the cut loci in order to guarantee uniqueness of trajectories for destinations sufficiently far from cut loci. Y1 - 2026 ER - TY - JOUR A1 - Wuttke, Ulrike A1 - Stockmann, Ralf A1 - Winkler, Alexander T1 - Podcasts als bibliothekarisches Handlungsfeld JF - B.I.T. online (BIT online) : Bibliothek, Information, Technologie Y1 - 2026 SN - 1616-2617 VL - 29 IS - 1 SP - 48 EP - 53 ER - TY - CHAP A1 - Sunkara, Vikram A1 - Rostami, Atefe A1 - von Tycowicz, Christoph A1 - Schütte, Christof T1 - Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI T2 - The 4th World Conference on Explainable Artificial Intelligence (XAI-2026) Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Schulte, Anton A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Stabernack, Benno T1 - A Flexible Open-Source Framework for FPGA-based Network-Attached Accelerators using SpinalHDL T2 - Architecture of Computing Systems - 39th International Conference, ARCS 2026, Mainz, Germany, March 24-26, 2026, Proceedings. N2 - Domain-specific accelerators are increasingly vital in heterogeneous computing systems, driven by the demand for higher computational capacity and especially energy efficiency. Network-attached FPGAs promise a scalable and flexible alternative to closely coupled FPGAs for integrating accelerators into computing environments. While the advantages of specialized hardware implementations are apparent, traditional hardware development and integration remain time-consuming and complex. We present an open-source framework which combines a hardware shell with supporting software libraries, which enables fast development and deployment of FPGA-based network-attached accelerators. In contrast to traditional approaches using VHDL or Verilog, we leverage generative programming with SpinalHDL, providing a flexible hardware description with multi-level abstractions. This work eases the integration of accelerators into existing network infrastructures and simplifies adaptation to different FPGAs, eliminating complex and lengthy top-level hardware descriptions. Y1 - 2026 ER -