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arXiv 2025年

作者： Kreikemeyer, Justin N. Jankowski, Milosz Wilsdorf, Pia Uhrmacher, Adelinde M. Institute for Visual and Analytic Computing University of Rostock Rostock Germany

Formal languages are an integral part of modeling and simulation. They allow the distillation of knowledge into concise simulation models amenable to automatic execution, interpretation, and analysis. However, the arguably most humanly accessible means of expressing models is through natural language, which is not easily interpretable by computers. Here, we evaluate how a Large Language Model (LLM) might be used for formalizing natural language into simulation models. Existing studies only explored using very large LLMs, like the commercial GPT models, without fine-tuning model weights. To close this gap, we show how an open-weights, 7B-parameter Mistral model can be fine-tuned to translate natural language descriptions to reaction network models in a domain-specific language, offering a self-hostable, compute-, and memory efficient alternative. To this end, we develop a synthetic data generator to serve as the basis for fine-tuning and evaluation. Our quantitative evaluation shows that our fine-tuned Mistral model can recover the ground truth simulation model in up to 84.5% of cases. In addition, our small-scale user study demonstrates the model’s practical potential for one-time generation as well as interactive modeling in various domains. While promising, in its current form, the fine-tuned small LLM cannot catch up with large LLMs. We conclude that higher-quality training data are required, and expect future small and open-source LLMs to offer new opportunities. © 2025, CC BY.

关键词： Modeling languages

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Potential and Challenges of Assurance Cases for Simulation Validation 24

Potential and Challenges of Assurance Cases for Simulation V...

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Proceedings of the Winter Simulation Conference

作者： Pia Wilsdorf Steffen Zschaler Fiete Haack Adelinde M. Uhrmacher Institute for Visual and Analytic Computing University of Rostock Rostock GERMANY Department of Informatics King's College London London UK

ISBN: (纸本)9798331534202

Simulation studies require thorough validation to ensure model accuracy, reliability, and credibility. While validation typically focuses on the simulation model itself, additional artifacts also influence study outcomes. Conceptual models, comprising research questions, requirements, inputs and outputs, model content, assumptions, and simplifications, provide context information for interpreting results and assessing model suitability. Validating other simulation artifacts for their fitness-for-purpose is complex, necessitating structured arguments to increase confidence. This paper explores when and how validation arguments should be constructed throughout the modeling and simulation lifecycle. Drawing on concepts from safety assurance cases, it defines key claims for the various artifacts, discusses validation arguments from different perspectives - including process, product, people, and project - and illustrates them through a computational biology case study. We conclude with a discussion of the suitability of such structured arguments for the comprehensive validation of simulation studies.

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Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

arXiv

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arXiv 2025年

作者： Jensen, Simon B. Oehmcke, Stefan Møgelmose, Andreas Madadi, Meysam Igel, Christian Escalera, Sergio Moeslund, Thomas B. Visual Analysis and Perception Laboratory Aalborg University Denmark Pioneer Centre for Artificial Intelligence Denmark Department of Computer Science Copenhagen University Denmark Institute for Visual & Analytic Computing Rostock University Germany University of Barcelona and Computer Vision Center Spain

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment. © 2025, CC BY.

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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A Foveated Framework to Accelerate Real-Time Path Tracing 10

A Foveated Framework to Accelerate Real-Time Path Tracing

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10th Eurographics Italian Chapter Conference on Smart Tools and Applications in Graphics, STAG 2023

作者： Mohanto, Bipul Kluge, Sven Staadt, Oliver University of Rostock Institute of Visual and Analytic Computing Germany

ISBN: (纸本)9783038682356

We developed a framework to accelerate real-time path tracing through foveated rendering, a robust technique that leverages human vision. Our dynamic foveated path-tracing framework integrates fixations and selectively lowers the rendering resolution towards the periphery. Through comprehensive experimentation, we demonstrated the effectiveness of our framework in this paper. Our solution can significantly enhance rendering performance, up to 25× without any notable visual differences. We further evaluated the framework using a structured error map algorithm with variable sample numbers. © 2023 The Author(s)

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Simulation-Supported Engineering of Self-Adaptive Software Systems

Simulation-Supported Engineering of Self-Adaptive Software S...

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2021 Winter Simulation Conference, WSC 2021

作者： Meyer, Tom Ruscheinski, Andreas Wilsdorf, Pia Uhrmacher, Adelinde M. Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Engineering a self-adaptive software system is challenging. During design-as well as run-time, assurance cases are central for ensuring reliable operation of the software. Simulation, in addition to software verification and testing, is a viable means to provide evidence for assurance cases. So far, little attention has been given to the development of underlying simulation models. Here, we argue that a systematic approach to develop simulation models will enhance the overall engineering process and will contribute to seamless integration of simulation and engineering processes. In our approach, we relate an explicit representation of the conceptual model and simulation experiments to artifacts of the engineering process. We will show first steps of applying our approach in a concrete ongoing software project for medical diagnosis, and discuss the role of components of the conceptual model in designing the software as a self-adaptive software system. © 2021 IEEE.

关键词： Diagnosis

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Simulation Experiment Schemas -Beyond Tools and Simulation Approaches

Simulation Experiment Schemas -Beyond Tools and Simulation A...

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2019 Winter Simulation Conference, WSC 2019

作者： Wilsdorf, Pia Dombrowsky, Marcus Uhrmacher, Adelinde M. Zimmermann, Julius Rienen, Ursula Van University of Rostock Institute for Visual and Analytic Computing Rostock18059 Germany

ISBN: (纸本)9781728132839

Simulation studies make use of various types of simulation experiments. For users, specifying these experiments is a demanding task since the specification depends on the experiment type and the idiosyncrasies of the used tools. Thus, we present an experiment generation procedure that guides users through the specification process, and abstracts away from the concrete execution environment. This abstraction is achieved by (1) developing schemas that define the properties of simulation experiments and dependencies between them, (2) specifying a mapping between schema properties and template fragments in the specification language of a target backend. We develop schema, template fragments, and mappings for stochastic discrete-event simulation, and show how the concepts can be transported to a different domain of modeling and simulation. Further, we expand the developed simple experiment schemas by a schema for experiment designs, and generate executable sensitivity analysis experiments, thereby demonstrating versatility and composability of our approach. © 2019 IEEE.

关键词： Mapping

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Inferring Dependency Graphs for Agent-Based Models Using Aspect-Oriented Programming

Inferring Dependency Graphs for Agent-Based Models Using Asp...

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2021 Winter Simulation Conference, WSC 2021

作者： Kreikemeyer, Justin Noah Koster, Till Uhrmacher, Adelinde M. Warnke, Tom Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Population-based CTMC models can generally be executed efficiently with stochastic simulation algorithms (SSAs). However, the heterogeneity in agent-based models poses a challenge for SSAs. To allow for an efficient simulation, we take SSAs that exploit dependency graphs for population-based models and adapt them to agent-based models. We integrate our approach with object-oriented frameworks for agent-based simulation by detecting dependencies via aspect-oriented programming (AOP). This way, modelers can implement models without manually recording dependency information, while still executing the models with efficient, dependency-aware SSAs. We provide an open-source implementation of our approach for the framework MASON, showing significant speedups in model execution. © 2021 IEEE.

关键词： Stochastic models

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Exploiting Provenance and Ontologies in Supporting Best Practices for Simulation Experiments: A Case Study on Sensitivity Analysis

Exploiting Provenance and Ontologies in Supporting Best Prac...

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2021 Winter Simulation Conference, WSC 2021

作者： Wilsdorf, Pia Fischer, Nadine Haack, Fiete Uhrmacher, Adelinde M. Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Simulation studies are intricate processes and user support for conducting more consistent, systematic, and efficient simulation studies is needed. Simulation experiments as one crucial part of a simulation study can benefit from semi-automatic method selection, parameterization, and execution. However, this largely depends on the context in which the experiment is conducted. Context information about a simulation study can be provided in form of provenance that documents which artifacts contributed in developing a simulation model. We present an approach that exploits provenance to support best practices for simulation experiments. The approach relies on 1) explicitly specified provenance information, 2) an ontology of methods, 3) best practices rules, and 4) integration with a previously developed experiment generation pipeline. We demonstrate our approach by conducting a sensitivity analysis experiment within a cell biological simulation study. © 2021 IEEE.

关键词： Ontology

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Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques

Exploring Time Series Segmentations Using Uncertainty and Fo...

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22nd Eurographics/IEEE VGT Conference on visualization, EuroVis 2020

作者： Bors, C. Eichner, C. Miksch, S. Tominski, C. Schumann, H. Gschwandtner, T. Institute of Visual Computing and Human-Centered Technology TU Wien Austria Institute for Visual and Analytic Computing University of Rostock Germany

ISBN: (纸本)9783038681069

Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing visual analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity. © 2020 The Author(s)

关键词： Uncertainty analysis

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