Machine Learning for Scientific Visualization: Ensemble Data Analysis

📝 Abstract

Scientific simulations and experimental measurements produce vast amounts of spatio-temporal data, yet extracting meaningful insights remains challenging due to high dimensionality, complex structures, and missing information. Traditional analysis methods often struggle with these issues, motivating the need for more robust, data-driven approaches. This dissertation explores deep learning methodologies to improve the analysis and visualization of spatio-temporal scientific ensembles, focusing on dimensionality reduction, flow estimation, and temporal interpolation. First, we address high-dimensional data representation through autoencoder-based dimensionality reduction for scientific ensembles. We evaluate the stability of projection metrics under partial labeling and introduce a Pareto-efficient selection strategy to identify optimal autoencoder variants, ensuring expressive and reliable low-dimensional embeddings. Next, we present FLINT, a deep learning model for high-quality flow estimation and temporal interpolation in both flow-supervised and flow-unsupervised settings. FLINT reconstructs missing velocity fields and generates high-fidelity temporal interpolants for scalar fields across 2D+time and 3D+time ensembles without domain-specific assumptions or extensive finetuning. To further improve adaptability and generalization, we introduce HyperFLINT, a hypernetwork-based approach that conditions on simulation parameters to estimate flow fields and interpolate scalar data. This parameter-aware adaptation yields more accurate reconstructions across diverse scientific domains, even with sparse or incomplete data. Overall, this dissertation advances deep learning techniques for scientific visualization, providing scalable, adaptable, and high-quality solutions for interpreting complex spatio-temporal ensembles.

💡 Analysis

Scientific simulations and experimental measurements produce vast amounts of spatio-temporal data, yet extracting meaningful insights remains challenging due to high dimensionality, complex structures, and missing information. Traditional analysis methods often struggle with these issues, motivating the need for more robust, data-driven approaches. This dissertation explores deep learning methodologies to improve the analysis and visualization of spatio-temporal scientific ensembles, focusing on dimensionality reduction, flow estimation, and temporal interpolation. First, we address high-dimensional data representation through autoencoder-based dimensionality reduction for scientific ensembles. We evaluate the stability of projection metrics under partial labeling and introduce a Pareto-efficient selection strategy to identify optimal autoencoder variants, ensuring expressive and reliable low-dimensional embeddings. Next, we present FLINT, a deep learning model for high-quality flow estimation and temporal interpolation in both flow-supervised and flow-unsupervised settings. FLINT reconstructs missing velocity fields and generates high-fidelity temporal interpolants for scalar fields across 2D+time and 3D+time ensembles without domain-specific assumptions or extensive finetuning. To further improve adaptability and generalization, we introduce HyperFLINT, a hypernetwork-based approach that conditions on simulation parameters to estimate flow fields and interpolate scalar data. This parameter-aware adaptation yields more accurate reconstructions across diverse scientific domains, even with sparse or incomplete data. Overall, this dissertation advances deep learning techniques for scientific visualization, providing scalable, adaptable, and high-quality solutions for interpreting complex spatio-temporal ensembles.

📄 Content

-Carl Jung A B S T R A C T Scientific simulations and experimental measurements produce vast amounts of spatio-temporal data, but extracting meaningful insights from such data remains a challenge due to its high dimensionality, complex structures, and missing information. Traditional analysis techniques often struggle with these issues, motivating the need for more robust, data-driven approaches. This dissertation explores deep learning methodologies to enhance the analysis and visualization of spatio-temporal scientific ensembles, focusing on dimensionality reduction, flow estimation, and temporal interpolation. First, we address the challenge of high-dimensional data representation by investigating autoencoder-based dimensionality reduction for scientific ensembles. We evaluate the stability of projection metrics under partial labeling and introduce a Pareto-efficient selection strategy to identify optimal autoencoder variants, ensuring expressive and reliable lowdimensional embeddings. Next, we present FLINT, a deep learning model designed for high-quality flow estimation and temporal interpolation in both flow-supervised and flow-unsupervised settings. FLINT reconstructs missing velocity fields and generates high-fidelity temporal interpolants for scalar fields across 2D+time and 3D+time ensembles, without requiring domain-specific assumptions or extensive finetuning. To further improve adaptability and generalization, we introduce HyperFLINT, a novel hypernetwork-based approach that dynamically conditions on simulation parameters to estimate flow fields and interpolate scalar data. This parameter-aware adaptation enables more accurate reconstructions across diverse scientific domains, even in cases of sparse or incomplete data. By addressing key challenges in scientific data analysis, this dissertation advances deep learning techniques for scientific visualization, providing scalable, adaptable, and high-quality solutions for interpreting complex spatio-temporal ensembles. v S A M E N VAT T I N G Wetenschappelijke simulaties en experimentele metingen produceren enorme hoeveelheden spatiotemporele data, maar het afleiden van zinvolle inzichten uit dergelijke data blijft een uitdaging vanwege de hoge dimensionaliteit, complexe structuren en ontbrekende informatie. Traditionele analysetechnieken worstelen vaak met deze problemen, wat de behoefte aan robuustere, datagestuurde benaderingen motiveert. Dit proefschrift onderzoekt deep learning-methodologieën om de analyse en visualisatie van spatiotemporele wetenschappelijke ensembles te verbeteren, met de nadruk op dimensionaliteitsreductie, stromingsschatting en temporele interpolatie. Ten eerste pakken we de uitdaging van hoogdimensionale datarepresentatie aan door autoencoder-gebaseerde dimensionaliteitsreductie voor wetenschappelijke ensembles te onderzoeken. We evalueren de stabiliteit van projectiemetrieken onder gedeeltelijke labeling en introduceren een Pareto-efficiënte selectiestrategie om optimale autoencodervarianten te identificeren, wat zorgt voor expressieve en betrouwbare laagdimensionale inbeddingen. Vervolgens presenteren we FLINT, een deep learning-model dat is ontworpen voor hoogwaardige stromingsschatting en temporele interpolatie in omstandigheden zowel met als zonder stromings-supervisie. FLINT reconstrueert ontbrekende snelheidsvelden en genereert zeer nauwkeurige temporele interpolanten voor scalaire velden in 2D+tijd-en 3D+tijdensembles, zonder dat domeinspecifieke aannames of uitgebreide fijnafstemming nodig zijn. Om de aanpasbaarheid en generalisatie verder te verbeteren, introduceren we HyperFLINT, een nieuwe, op hypernetwerken gebaseerde aanpak die dynamische voorwaarden stelt aan simulatieparameters om stromingsvelden te schatten en scalaire data te interpoleren. Deze parameter-inclusieve aanpassing maakt nauwkeurigere reconstructies mogelijk in diverse wetenschappelijke domeinen, zelfs in gevallen van schaarse of onvolledige data. Door belangrijke uitdagingen in wetenschappelijke data-analyse aan te pakken, bevordert dit proefschrift deep learning-technieken voor wetenschappelijke visualisatie en biedt het schaalbare, aanpasbare en hoogwaardige oplossingen voor de interpretatie van complexe spatiotemporele ensembles. Computer Graphics Forum (2025).

Additionally, contributed as a co-author to the following projects:

Advancements in computing and data acquisition technologies have led to a rapid increase in the size and complexity of scientific datasets.

From high-resolution simulations in fluid dynamics to large-scale astrophysical models and experimental imaging, scientific data often consist of measurements or simulation outputs defined over spatial or spatio-temporal domains, typically represented as multidimensional fields such as scalar, vector, or tensor quantities distributed over 2D or 3D grids evolving in time.

In many scientific applications, individual datasets are not sufficient to capture variability or uncerta

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