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-
GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
A JAX-based differentiable BEM solver matches traditional BEM accuracy on benchmarks and supports gradient-driven acoustic geometry optimization.
A new preparedness metric for ambulance fleet operations under uncertainty enables optimized selection and reassignment decisions and outperforms nine existing methods on real emergency medical service data.
citing papers explorer
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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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NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
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JAX-BEM: Gradient-Based Acoustic Shape Optimisation via a Differentiable Boundary Element Method
A JAX-based differentiable BEM solver matches traditional BEM accuracy on benchmarks and supports gradient-driven acoustic geometry optimization.
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Policies for the Operation of an Ambulance Fleet under Uncertainty based on a New Preparedness Metric
A new preparedness metric for ambulance fleet operations under uncertainty enables optimized selection and reassignment decisions and outperforms nine existing methods on real emergency medical service data.