  In a quest towards an intelligent decision-making machine, the ability to
make plausible predictions is the central pillar of its intelligence. A
predicting algorithm's central idea is to understand the governing physical
rules and make plausible and apt predictions based on the same governing laws.
Extending the study towards the astrophysical phenomenon puts the model's
ability to test since the model has to understand various parameters that
govern the dynamics of the event and understand the spatial and temporal
evolution by applying the plausible laws. This work presents a deep learning
model to predict plausible future events that maintain spatial and temporal
coherence. We have trained over two broad classes, the evolution of Sa, Sb, S0,
and Sd galaxy mergers and evolution of gravitational lenses with a higher
redshift of the foreground galaxy having $15M_{\odot}$. We extended our work
towards developing a direct measure of the performance metric for any
prediction algorithm. We thereby introduce a novel metric, Correctness Factor
(CF), which directly outputs how accurate a prediction is.
