# NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

arXiv: https://arxiv.org/abs/1904.09044 · HTML: https://mlsanity.com/paper/1904.09044/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations](https://mlsanity.com/paper/2202.08956/) (2022)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations](https://mlsanity.com/paper/2207.13091/) (2022)
- [Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration](https://mlsanity.com/paper/2504.00904/) (2025)
- [ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging](https://mlsanity.com/paper/2407.14100/) (2024)
- [HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns](https://mlsanity.com/paper/2305.07859/) (2023)
- [ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification](https://mlsanity.com/paper/2504.02919/) (2025)
- [SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing](https://mlsanity.com/paper/2511.15182/) (2025)
- [Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling](https://mlsanity.com/paper/2606.15959/) (2026)
- [Neural Stream Functions](https://mlsanity.com/paper/2307.08142/) (2023)
- [Seeing the Many: Exploring Parameter Distributions Conditioned on Features in Surrogates](https://mlsanity.com/paper/2508.13088/) (2025)
- [Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields](https://mlsanity.com/paper/2602.15155/) (2026)

## Abstract (arXiv)

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.