VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations (2207.13091v3)

Abstract: We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computationally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis. To improve the efficient use of computational resources and support high-resolution exploration, we perform ray casting from different viewpoints to collect samples and produce compact latent representations. This latent encoding process reduces the cost of surrogate model training while maintaining the output quality. In the model training stage, we select viewpoints to cover the whole viewing sphere and train corresponding VDL-Surrogate models for the selected viewpoints. In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space. For any given viewpoint, we make interpolations over decoded data at selected viewpoints and generate visualizations with user-specified visual mappings. We show the effectiveness and efficiency of VDL-Surrogate in cosmological and ocean simulations with quantitative and qualitative evaluations. Source code is publicly available at https://github.com/trainsn/VDL-Surrogate.

• The paper introduces VDL-Surrogate, a view-dependent latent-based model that significantly reduces computational cost while providing high-fidelity simulation visualizations.
• The methodology employs ray casting and viewpoint-specific latent representations to efficiently train models and interpolate simulation outputs.
• Applications in cosmology and oceanography demonstrate its superior accuracy and efficiency over traditional methods, enabling flexible post-hoc visual mappings.

Overview of VDL-Surrogate: A View-Dependent Latent-based Model for Ensemble Simulations

The paper introduces VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model created for parameter space exploration of ensemble simulations, providing both high-resolution visualizations and flexibility in user-specified visual mappings. Traditional ensemble simulations, widely utilized in fields like cosmology and oceanography, often demand considerable computational resources and time, making extensive parameter explorations impractical. By leveraging surrogate modeling, VDL-Surrogate significantly enhances efficiency by predicting simulation results from a given set of parameters, thus obviating the need for computationally costly simulations.

View-Dependent Latent Representation

The core innovation of VDL-Surrogate lies in its use of view-dependent latent representations. These representations substantially mitigate computational overhead, allowing for more efficient model training and maintaining high-quality output. The process involves ray casting from various viewpoints to generate compact latent representations, which serve to both lower the cost of training and enhance output fidelity. During the training phase, specific viewpoints are selected to cover the viewing sphere comprehensively, and corresponding VDL-Surrogate models are trained. For inference, latent representations are predicted for these viewpoints, decoded into the data space, and interpolated to generate visualizations based on user-specified mappings.

Practical Applications and Evaluation

VDL-Surrogate’s effectiveness has been demonstrated through applications in both cosmological and oceanographic simulations, where it provided high-quality visual outputs with resource-efficient computational demand. The model's ability to process large datasets like Nyx and MPAS-Ocean has proven its capability to operate at scales relevant to real-world scientific problems. Quantitative evaluations detailed in the paper illustrate that the surrogate model reliably predicts simulation output, thereby enabling intricate parameter space exploration. The results from these simulations indicate significant improvements in efficiency and accuracy compared to prior methods, such as InSituNet, especially in high-resolution contexts.

Implications and Future Directions

VDL-Surrogate not only addresses the inefficiencies faced by surrogate models focused on either data-space or image-space but also provides a flexible approach to visual mapping independent of prior constraints. This flexibility is crucial for scientists seeking to explore new phenomena by adjusting visual parameters post-hoc. The application potential cannot be understated, given the increasing complexities and scales of scientific simulations.

Future advancements could include the exploration of more generalizable view-dependent sampling strategies or incorporating broader aspects of neural network architectures, such as graph neural networks, to further enhance computational efficiency. Additionally, given the computational demands of ensemble simulations, advancements in GPU utilization and distributed computing could be leveraged to further optimize performance.

In summary, VDL-Surrogate offers a robust approach to handling large-scale, high-resolution simulations, maintaining high-fidelity visualizations with reduced computational needs, and enhancing the user's ability to navigate and analyze complex parameter spaces effectively. This paper contributes an important methodological advancement with theoretical implications for surrogate modeling and practical applications across multiple domains.