numerical analysis
PulseAugur coverage of numerical analysis — every cluster mentioning numerical analysis across labs, papers, and developer communities, ranked by signal.
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Two arXiv papers analyze statistical inverse problems in AI and ML
Two new research papers submitted to arXiv explore statistical inverse problems within machine learning and artificial intelligence. The first paper focuses on regularization techniques for these problems in non-reflexi…
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New algorithm simplifies graph node selection for large-scale network analysis
Researchers have developed a new algorithm for selecting representative nodes from large graphs, a crucial task in network analysis. This method, termed Scalable Graph Coreset Selection via Greedy Sampling, bypasses the…
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New method reconstructs 3D geometry for direct CAD and simulation use
Researchers have developed FORGE-SIM, a novel method for reconstructing 3D geometry from sparse RGB images that is directly compatible with CAD and simulation workflows. This approach optimizes a multi-patch B-spline bo…
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New zero-one law simplifies one-shot system identification
Researchers have developed a new method for identifying analytic systems from a single experiment, applicable to systems linearly parameterized by prescribed dictionary terms. They proved a sharp zero-one law, indicatin…
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New formulation gap identified in physics-informed learning for multiscale equations
Researchers have identified a statistical formulation gap in physics-informed learning for nonlinear multiscale elliptic equations. They proved a finite-sample error bound for a variational neural solver, showing that s…
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New research details kernel-based operator learning with error analysis and physics-informed extensions
Researchers have published new work on kernel-based operator learning, detailing error analysis and budget allocation strategies. The study introduces a two-stage framework involving offline regression and online recons…
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Neural networks accelerate pseudospectra computation for stability analysis
Researchers have developed a novel neural network approach to accelerate the computation of pseudospectra for structured non-normal banded matrices. This method predicts spectrally sensitive regions, allowing for focuse…
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New ADANNs method enhances deep learning for parametric partial differential equations
Researchers have introduced Algorithmically Designed Artificial Neural Networks (ADANNs), a novel deep learning approach for approximating operators related to parametric partial differential equations. This method comb…