Deterministic sketching via row subset selection produces subspace embeddings with probability 1 for Krylov methods and yields performance comparable to randomized sketching for matrix functions, linear systems, and eigenvalue problems.
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This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.
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Deterministic sketching for Krylov subspace methods
Deterministic sketching via row subset selection produces subspace embeddings with probability 1 for Krylov methods and yields performance comparable to randomized sketching for matrix functions, linear systems, and eigenvalue problems.
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Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.