SOURCE-LINKED INTELLIGENCE
Can Deep Learning Achieve Cross-Physics Mapping?
Can deep learning translate physical fields governed by fundamentally different equations? We address this question by introducing Cross-Physics Mapping (CPM), an operator-learning framework for mappings between heterogeneous physical domains. We formulate sufficient conditions for such mappings through compatible latent representations and propose a dimensionless scaling principle that aligns the characteristic evolution scales of the source and target systems without assuming their dynamical equivalence. As a representative test, paired diffusion and wave fields are generated independently f
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T08:45:23.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.