arXiv subjects

Benjamin Uekermann

Publications and source records attributed to Benjamin Uekermann.

8 recordsLinked to original sources

arXiv2026

Orchestrating Black-Box Schema Converters: An Empirical Study of Automated, Quality-Ranked Conversion Across Heterogeneous Schema Languages

Modern software systems routinely need the same data model in several schema languages: a model may exist as JSON Schema for a web API, as XSD for data exchange, and as SHACL for a knowledge graph. Keeping these representations consistent as the model evolves is a recurring construction and maintenance burden, because converters between schema languages are hard to find, scattered across ecosystems, of uneven quality, and frequently lossy. We study, empirically, to what extent such imperfect, heterogeneous converters can be orchestrated into automated, reproducible, and quality-ranked conversions, and where the current converter landscape reaches its limits. Our approach models schema languages as nodes and converters, treated as black boxes, as directed edges, so that conversions become paths that are discovered, executed, ranked, and reported with full per-step provenance, with failures handled by trying alternatives. We realize it as the open-source Schema Conversion Orchestrator, integrate it into MetaConfigurator, and evaluate it on 60 conversion tasks built from real-world schemas across five schema languages, using agent-assisted, human-reviewed quality annotations. Orchestration surfaces a usable result for 43 of 60 tasks; the remaining failures localize concrete gaps in the converter landscape. We discuss implications for tool builders and for measuring conversion quality.

cs.SE↗

arXiv2026

MetaConfigurator: AI-Assisted RDF Authoring from JSON Data

Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability. While JSON Schema ensures structural validation, it provides no native support for Linked Data semantics. This paper presents an RDF Authoring View extending the open-source JSON Schema editor MetaConfigurator, enabling researchers to transform existing JSON, YAML, or CSV data into RDF using AI-assisted RML mappings, refine triples, execute SPARQL queries, visualize knowledge graphs, and export RDF serializations within a single integrated web interface. This workflow is supported by ontology-aware IRI auto-completion, bidirectional synchronization between JSON-LD text views and RDF triple tables, and AI-assisted SPARQL query generation from natural language hints. We demonstrate the workflow using laboratory data from metal-organic framework (MOF) synthesis experiments. Protocol data describing reagents, procedure steps, and quantities is converted from JSON to ontology-based JSON-LD via RML mappings. We then refine the semantic representation, query relationships between experimental conditions and outcomes, and explore the resulting knowledge graph interactively. This integrated environment bridges conventional structured data management with Semantic Web technologies while preserving experimental context and lowering technical barriers through AI assistance.

cs.SE↗

arXiv2025

Evaluation of preCICE (version 3.3.0) in an Earth System Model Regridding Benchmark

In Earth System Modeling (ESM), meshes of different models usually do not match, requiring data mapping algorithms implemented in coupling software. Valcke et al. recently introduced a benchmark to evaluate such algorithms and compared implementations in four specialized ESM couplers. In this paper, we assess preCICE, a general-purpose coupling library not limited to ESM, using this benchmark and compare our results to the original study. The generality of preCICE with its larger community offers potential benefits to ESM applications, but the software naturally lacks ESM-specific solutions. We describe necessary pre- and postprocessing steps to make the benchmark tangible for preCICE. Overall, preCICE achieves comparable results; using its radial basis function mapping yields significantly lower errors.

physics.ao-ph↗

arXiv2025

A waveform iteration implementation for black-box multi-rate higher-order coupling

Many multiphysics simulations involve processes evolving on disparate time scales, posing a challenge for efficient coupling. A naive approach that synchronizes all processes using the smallest time scale wastes computational resources on slower processes and typically achieves only linear convergence in time. Waveform iteration is a promising numerical technique that enables higher-order, multi-rate coupling while treating coupled components as black boxes. However, applying this approach to PDE-based coupled simulations is nontrivial. In this paper, we integrate waveform iteration into the black-box coupling library preCICE with minimal modifications to its API. We detail how this extension interacts with key preCICE features, including data mapping for non-matching meshes, quasi-Newton acceleration for strongly coupled problems, and parallel peer-to-peer communication. We then showcase that waveform iteration significantly reduces numerical errors -- often by orders of magnitude. This advancement greatly enhances preCICE, benefiting its extensive user community.

cs.MS↗

arXiv2025

AI-assisted JSON Schema Creation and Mapping

Model-Driven Engineering (MDE) places models at the core of system and data engineering processes. In the context of research data, these models are typically expressed as schemas that define the structure and semantics of datasets. However, many domains still lack standardized models, and creating them remains a significant barrier, especially for non-experts. We present a hybrid approach that combines large language models (LLMs) with deterministic techniques to enable JSON Schema creation, modification, and schema mapping based on natural language inputs by the user. These capabilities are integrated into the open-source tool MetaConfigurator, which already provides visual model editing, validation, code generation, and form generation from models. For data integration, we generate schema mappings from heterogeneous JSON, CSV, XML, and YAML data using LLMs, while ensuring scalability and reliability through deterministic execution of generated mapping rules. The applicability of our work is demonstrated in an application example in the field of chemistry. By combining natural language interaction with deterministic safeguards, this work significantly lowers the barrier to structured data modeling and data integration for non-experts.

cs.SE↗

arXiv2021

preCICE v2: A Sustainable and User-Friendly Coupling Library

preCICE is a free/open-source coupling library. It enables creating partitioned multi-physics simulations by gluing together separate software packages. This paper summarizes the development efforts in preCICE of the past five years. During this time span, we have turned the software from a working prototype -- sophisticated numerical coupling methods and scalability on ten thousands of compute cores -- to a sustainable and user-friendly software project with a steadily-growing community. Today, we know through forum discussions, conferences, workshops, and publications of more than 100 research groups using preCICE. We cover the fundamentals of the software alongside a performance and accuracy analysis of different data mapping methods. Afterwards, we describe ready-to-use integration with widely-used external simulation software packages, tests and continuous integration from unit to system level, and community building measures, drawing an overview of the current preCICE ecosystem.

cs.MS↗

arXiv2021

FEniCS-preCICE: Coupling FEniCS to other Simulation Software

The new software FEniCS-preCICE is a middle software layer, sitting in between the existing finite-element library FEniCS and the coupling library preCICE. The middle layer simplifies coupling (existing) FEniCS application codes to other simulation software via preCICE. To this end, FEniCS-preCICE converts between FEniCS and preCICE mesh and data structures, provides easy-to-use coupling conditions, and manages data checkpointing for implicit coupling. The new software is a library itself and follows a FEniCS-native style. Only a few lines of additional code are necessary to prepare a FEniCS application code for coupling. We illustrate the functionality of FEniCS-preCICE by two examples: a FEniCS heat conduction code coupled to OpenFOAM and a FEniCS linear elasticity code coupled to SU2. The results of both scenarios are compared with other simulation software showing good agreement.

cs.MS↗

arXiv2020

Quasi-Newton Waveform Iteration for Partitioned Fluid-Structure Interaction

We present novel coupling schemes for partitioned multi-physics simulation that combine four important aspects for strongly coupled problems: implicit coupling per time step, fast and robust acceleration of the corresponding iterative coupling, support for multi-rate time stepping, and higher-order convergence in time. To achieve this, we combine waveform relaxation -- a known method to achieve higher order in applications with split time stepping based on continuous representations of coupling variables in time -- with interface quasi-Newton coupling, which has been developed throughout the last decade and is generally accepted as a very robust iterative coupling method even for gluing together black-box simulation codes. We show convergence results (in terms of convergence of the iterative solver and in terms of approximation order in time) for two academic test cases -- a heat transfer scenario and a fluid-structure interaction simulation. We show that we achieve the expected approximation order and that our iterative method is competitive in terms of iteration counts with those designed for simpler first-order-in-time coupling.

math.NA↗