Forward-mode automatic differentiation replaces finite-difference approximations for Jacobian-vector products in JFNK solvers, delivering 2-3 orders of magnitude speedup and lifting minimum solver completion from 42% to 95% across Burgers, radiation diffusion, reaction-diffusion, and nonlinear time-
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A PINN pretrained on mechanistic synthetic data and fine-tuned experimentally is deployed in an EKF-style filter to estimate separator phase heights from flow rates alone.
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Robust Matrix-Free Newton-Krylov Solvers via Automatic Differentiation
Forward-mode automatic differentiation replaces finite-difference approximations for Jacobian-vector products in JFNK solvers, delivering 2-3 orders of magnitude speedup and lifting minimum solver completion from 42% to 95% across Burgers, radiation diffusion, reaction-diffusion, and nonlinear time-
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Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
A PINN pretrained on mechanistic synthetic data and fine-tuned experimentally is deployed in an EKF-style filter to estimate separator phase heights from flow rates alone.