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Found 4 papers, 2 papers with code
Spatiotemporal k-means
Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects.
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
Deeptime: a Python library for machine learning dynamical models from time series data
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
We propose spectral methods for long-term forecasting of temporal signals stemming from linear and nonlinear quasi-periodic dynamical systems.
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