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Found 8 papers, 7 papers with code
Generative learning for nonlinear dynamics
Emerging interdisciplinary works bridge nonlinear dynamics and learning theory, such as operator-theoretic methods for complex fluid flows, or detection of broken detailed balance in biological datasets.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Model scale versus domain knowledge in statistical forecasting of chaotic systems
Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics.
Recurrences reveal shared causal drivers of complex time series
Many experimental time series measurements share unobserved causal drivers.
Chaos as an interpretable benchmark for forecasting and data-driven modelling
Our dataset is annotated with known mathematical properties of each system, and we perform feature analysis to broadly categorize the diverse dynamics present across the collection.
Deep reconstruction of strange attractors from time series
Experimental measurements of physical systems often have a limited number of independent channels, causing essential dynamical variables to remain unobserved.
Lagrange2D: A Mathematica package for Lagrangian analysis of two-dimensional fluid flows
We introduce Lagrange2D, a Mathematica package for analysis and characterization of complex fluid flows using Lagrangian transport metrics.
Fluid Dynamics Computational Physics
Cellular automata as convolutional neural networks
This motivates our development of a general convolutional multilayer perceptron architecture, which we find can learn the dynamical rules for arbitrary CA when given videos of the CA as training data.
