1,214 research outputs found
Data-driven discovery of coordinates and governing equations
The discovery of governing equations from scientific data has the potential
to transform data-rich fields that lack well-characterized quantitative
descriptions. Advances in sparse regression are currently enabling the
tractable identification of both the structure and parameters of a nonlinear
dynamical system from data. The resulting models have the fewest terms
necessary to describe the dynamics, balancing model complexity with descriptive
ability, and thus promoting interpretability and generalizability. This
provides an algorithmic approach to Occam's razor for model discovery. However,
this approach fundamentally relies on an effective coordinate system in which
the dynamics have a simple representation. In this work, we design a custom
autoencoder to discover a coordinate transformation into a reduced space where
the dynamics may be sparsely represented. Thus, we simultaneously learn the
governing equations and the associated coordinate system. We demonstrate this
approach on several example high-dimensional dynamical systems with
low-dimensional behavior. The resulting modeling framework combines the
strengths of deep neural networks for flexible representation and sparse
identification of nonlinear dynamics (SINDy) for parsimonious models. It is the
first method of its kind to place the discovery of coordinates and models on an
equal footing.Comment: 25 pages, 6 figures; added acknowledgment
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
Understanding the dynamical processes that govern the performance of
functional materials is essential for the design of next generation materials
to tackle global energy and environmental challenges. Many of these processes
involve the dynamics of individual atoms or small molecules in condensed
phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten
atoms at interfaces, etc., which are difficult to understand due to the
complexity of local environments. In this work, we develop graph dynamical
networks, an unsupervised learning approach for understanding atomic scale
dynamics in arbitrary phases and environments from molecular dynamics
simulations. We show that important dynamical information can be learned for
various multi-component amorphous material systems, which is difficult to
obtain otherwise. With the large amounts of molecular dynamics data generated
everyday in nearly every aspect of materials design, this approach provides a
broadly useful, automated tool to understand atomic scale dynamics in material
systems.Comment: 25 + 7 pages, 5 + 3 figure
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