soft-matter-physics
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deep-learning-active-flows
deep-learning-active-flows PublicThis repository contains the codebase accompanying the manuscript "Inferring activity from the flow field around active colloidal particles using deep learning", submitted to J. Fluid Mech.. The co…
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optimal-control-active-particles
optimal-control-active-particles PublicCode and data for optimal transport of an active colloidal particle near a no-slip wall using a Ritz–Chebyshev genetic algorithm with GPU-accelerated trajectory optimization.
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optimal-control-active-particle
optimal-control-active-particle PublicForked from soft-matter-physics/optimal-control-active-particles
Code and data for optimal transport of an active colloidal particle near a no-slip wall using a Ritz–Chebyshev genetic algorithm with GPU-accelerated trajectory optimization.
Jupyter Notebook
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- optimal-control-active-particle Public Forked from soft-matter-physics/optimal-control-active-particles
Code and data for optimal transport of an active colloidal particle near a no-slip wall using a Ritz–Chebyshev genetic algorithm with GPU-accelerated trajectory optimization.
- optimal-control-active-particles Public
Code and data for optimal transport of an active colloidal particle near a no-slip wall using a Ritz–Chebyshev genetic algorithm with GPU-accelerated trajectory optimization.
- deep-learning-active-flows Public
This repository contains the codebase accompanying the manuscript "Inferring activity from the flow field around active colloidal particles using deep learning", submitted to J. Fluid Mech.. The code reproduces the main results, figures, and simulations presented in the paper. It is organized for clarity and ease of reproducibility.
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