Tangent-SBM：用机制敏感性学习随机输运的切空间薛定谔桥匹配

View PDF HTML (experimental)

Abstract:Predicting how stochastic systems respond to changes in viscosity, reaction rates, or external forces requires costly simulations, motivating reusable learned models. Yet matching observed outcome distributions does not ensure accurate intervention responses. We introduce Tangent Schrödinger Bridge Matching (Tangent-SBM), which learns stochastic transports from endpoint observations and mechanistic sensitivities. It propagates parameter derivatives alongside trajectories and supervises them against supplied targets. For average-response targets, single-rollout squared error also penalizes response variability; our objective uses two independent rollouts to match the mean without this additional penalty. We establish conditions under which sensitivity accuracy bounds finite-change prediction error and decision regret. Across Gaussian, stochastic double-well, PDEBench reaction--diffusion, and stochastic Navier--Stokes systems, Tangent-SBM improves sensitivity and finite-change prediction over matched conditional-bridge baselines while maintaining comparable endpoint and distributional accuracy. Controls examine target correctness, response objectives, and simulator-budget allocation. To test decision usefulness, we evaluate calibrated viscosity selection in Navier--Stokes: Tangent-SBM reduces tracking error relative to taking no action on every evaluated task.

Subjects:	Machine Learning (cs.LG)
ACM classes:	I.2.6; G.3
Cite as:	arXiv:2610.02906 [cs.LG]
	(or arXiv:2610.02906v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.02906 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jowaria Khan [view email]
[v1] Fri, 2 Oct 2026 06:56:25 UTC (2,302 KB)