Graduate Methods to interpolate surfaces from gradient field?

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The discussion focuses on interpolating a 2D function from a grid of vectors representing average headings in a spatial domain, generated through stochastic simulations for a robotic search algorithm. The goal is to approximate a gradient field to identify basins of attraction for locating chemical sources. The author seeks standard methods for estimating a 2D function from gradient data and questions whether simpler approaches exist for analyzing the basins of attraction from the vector grid. A reference to a related question on Stack Exchange is provided for additional context. The inquiry highlights the need for effective techniques in gradient-based function estimation.
grumpymrgruff
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I have a 2D regular grid of vectors representing average headings on a 2D spatial domain. These are generated by stochastic simulation of chemical-sampling and gradient-estimation techniques for a robotic search algorithm seeking a chemical source.

Without going into a lot of detail, I would like to treat this grid of robot headings as an approximation of a gradient field. Ideally, I want to interpolate the function from its gradient and use it to determine basins of attraction where my search algorithm converges to "true" chemical sources.

What I don't know is if their are any standard methods for estimating a 2D function given a grid of gradient data.

Does anyone know of any? Or perhaps I'm over-complicating things and there are simpler ways to estimate the basins of attraction (areas and morphologies) from this regular grid of vector data?

Thanks!
 
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