chemical process modelers

Abstract: Physics Informed Neural Networks offer a mesh-free approach to solving differential equations, but their application to time-dependent problems remains challenging, particularly when the governing dynamics involve widely different time scales. With collocation points spread uniformly in time, short intervals of rapid change are under resolved. This work introduces an equation informed time reparameterization framework that modifies the temporal coordinate used during PINN training based on the local dynamics of the governing equation. The proposed method follows a two stage procedure. First, a pilot PINN training provides an approximate solution, which is used to linearize the governing differential operator and estimate its local response to small perturbations. These responses are combined into a time-dependent rate function, large where the dynamics are fast, that defines a strictly increasing transformation from physical time to a new computational time coordinate. A second PINN is then trained on the transformed governing equation, with uniformly sampled collocation points in the new coordinate producing a nonuniform distribution in physical time. Different combination rules and spatial reduction strategies are considered. With the cost of the pilot training included, the method reduces the error by a factor of 4 to 29 relative to the best alternative method on the Kachanov Rabotnov creep-damage model near rupture, and by a factor of 5 to 16 relative to every alternative method on the Frank-Kamenetskii equation near blow up.

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.