TensorFlow Chaotic Prediction and Blow Up
This addresses a challenge in chaotic system prediction for researchers, but it is incremental as it highlights a library-specific issue rather than a novel method.
The paper tackled predicting chaotic system dynamics using TensorFlow, achieving short-term prediction but discovering that long-term predictions blow up due to TensorFlow's nondeterministic behavior.
Predicting the dynamics of chaotic systems is one of the most challenging tasks for neural networks, and machine learning in general. Here we aim to predict the spatiotemporal chaotic dynamics of a high-dimensional non-linear system. In our attempt we use the TensorFlow library, representing the state of the art for deep neural networks training and prediction. While our results are encouraging, and show that the dynamics of the considered system can be predicted for short time, we also indirectly discovered an unexpected and undesirable behavior of the TensorFlow library. More specifically, the longer term prediction of the system's chaotic behavior quickly deteriorates and blows up due to the nondeterministic behavior of the TensorFlow library. Here we provide numerical evidence of the short time prediction ability, and of the longer term predictability blow up.