Quantum-inspired classical algorithms provably cannot achieve exponential-in-dimension speedup for finite-element discretizations of the high-dimensional Poisson equation.
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2026 5representative citing papers
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
EDA-specific RHS differences used as sketching matrix for randomized Hessian preconditioner accelerate linear solves across the ensemble in Lorenz-96 experiments.
Model order reduction with snapshots from high-fidelity or one-shot solves accelerates 3D thermal topology optimization by up to 16x versus standard high-fidelity workflows and 1.54x versus one-shot alone.
citing papers explorer
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Quantum-inspired methods for finite-element discretizations of the high-dimensional Poisson equation
Quantum-inspired classical algorithms provably cannot achieve exponential-in-dimension speedup for finite-element discretizations of the high-dimensional Poisson equation.
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Learning Adaptive Coarse Spaces Using Transferable Neural Network Models for Linear and Nonlinear Overlapping Domain Decomposition Methods
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
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Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
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Accelerating an ensemble of variational data assimilations with randomized preconditioning
EDA-specific RHS differences used as sketching matrix for randomized Hessian preconditioner accelerate linear solves across the ensemble in Lorenz-96 experiments.
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Comparison of model order reduction techniques with one-shot procedure for topology optimization for thermal applications
Model order reduction with snapshots from high-fidelity or one-shot solves accelerates 3D thermal topology optimization by up to 16x versus standard high-fidelity workflows and 1.54x versus one-shot alone.