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In situ visualization and steering of computational modeling can beeffectively achieved using reactive programming, which leverages temporalabstraction and data caching mechanisms to create dynamic workflows. However,implementing a temporal cache for large-scale simulations can be challenging.Implicit neural networks have proven effective in compressing large volumedata. However, their application to distributed data has yet to be fullyexplored. In this work, we develop an implicit neural representation fordistributed volume data and incorporate it into the DIVA reactive programmingsystem. This implementation enables us to build an in situ temporal cachingsystem with a capacity 100 times larger than previously achieved. We integrateour implementation into the Ascent infrastructure and evaluate its performanceusing real-world simulations.

Distributed Neural Representation for Reactive in situ Visualization