Generative Inverse Design with Abstention via Diagonal Flow Matching
Inverse design aims to find design parameters $x$ achieving target performance $y^*$. Generative approaches learn bidirectional mappings between designs and labels, enabling diverse solution sampling. However, standard conditional flow matching (CFM), when adapted to inverse problems by pairing labels with design parameters, exhibits strong sensitivity to their arbitrary ordering and scaling, leading to unstable training. We introduce Diagonal Flow Matching (Diag--CFM), which resolves this through a zero-anchoring strategy that pairs design coordinates with noise and labels with zero, making the learning problem provably invariant to coordinate permutations. This yields substantially lowe...
| Authors | Miguel de Campos, Werner Krebs, Hanno Gottschalk |
|---|---|
| Primary category | cs.LG |
| Published | 16 March 2026 |
| Last updated at source | 4 July 2026 |
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