AI RESEARCH
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
arXiv CS.AI
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ArXi:2605.09511v1 Announce Type: new Many downstream decisions in complex terrain require fast wind estimates at a small number of user-specified locations and heights for a given forecast valid time, rather than another dense forecast field on a fixed grid. We present WindINR, a latent-state implicit neural representation framework for continuous high-resolution local wind query and sparse-observation correction. WindINR maps static terrain descriptors, a low-resolution background field, and continuous query coordinates to a high-resolution wind state through a latent-conditioned decoder.