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.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Khan, J., & Bondi-Kelly, E. (2026). Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities. https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities
MLA 9
Khan, Jowaria, and Elizabeth Bondi-Kelly. "Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities." https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities.
Chicago (author–date)
Khan, Jowaria, and Elizabeth Bondi-Kelly. 2026. "Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities." https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities.
Harvard
Khan, J. and Bondi-Kelly, E. (2026) 'Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities', Available at: https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities.
Vancouver
Khan J, Bondi-Kelly E. Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities. https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities
IEEE
J. Khan, and E. Bondi-Kelly, "Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities," https://omanscience.com/en/articles/tangent-schr-dinger-bridge-matching-learning-stochastic-transport-with-mechanistic-sensitivities.