2020-02-14 - Adam Rupe  - Intrinsic computation and physics-based ML for emergent  ...
Channel: NERSC
Date: 2020-02-15

NERSC Data Seminars: https://github.com/NERSC/data-seminars

Title: Intrinsi ccomputation and physics-based machine learning for emergent self-organization in far-from-equilibrium systems

Abstract: Coherent structures form spontaneously in far-from-equilibrium spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean and atmosphere dynamics. Phenomenologically, they appear as key components that organize macroscopicdynamical behaviors. Unlike their equilibrium and near-equilibriumc ounterparts, there is no general theory to predict what patterns and structures may emerge in far-from-equilibrium systems. Each system behaves differently; details and history matter. The complex behaviors that emerge cannot be explicitly described mathematically, nor can they be directly deduced from the governing equations (e.g. what is the mathematical expression for a hurricane, and how can you derive it from the equations of a general circulation climate model?). It is thus appealing to bring the instance-based data-driven models of machine learning to bear on the problem. Supervised learning models have been the most successful, but they require ground-truth training labels which do not exist for far-from-equilibrium structures. Unsupervised models that leverage physical principles of self-organization are required. To this end we will make connections between structural organization and intrinsic computation to motivate the use of physics-based unsupervised models called local causal states. As local models they are capable of capturing structures of arbitrary shape and size in a visually interpretable manner, due to the shared coordinate geometry between observable spacetime fields and their associated latent local causal state fields. We will show the local causal states can capture patterns in cellular automata models as generalized spacetime symmetries and coherent structures as localized deviations from these generalized symmetries. To demonstrate their applicability to real-world systems, we show the utility of the local causal states for extracting coherent structures in simulations and observations of complex fluid flows, including promising results highlighting extreme weather events in the water vapor field of the CAM5.1 climate model. These results require high-performance computing, and we will briefly describe how we were able to process almost 90TB in under 7 minutes end-to-end on 1024 Haswell nodes of Cori using a distributed implementation in Python.