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🏛️ The Numerical Analysis Crypt
math.NA: Where Numerical Analysis papers rest without their code.
669
Total Papers
594
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1
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74
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11.1%
Survival Rate
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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
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PDE-Net: Learning PDEs from Data
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Efficient tensor completion for color image and video recovery: Low-rank tensor train
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Tensor Ring Decomposition
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Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
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Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
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Probabilistic Numerics and Uncertainty in Computations
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Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
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Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
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Data Driven Governing Equations Approximation Using Deep Neural Networks
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Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
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Practical sketching algorithms for low-rank matrix approximation
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A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
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Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations
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A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
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Multigrid with rough coefficients and Multiresolution operator decomposition from Hierarchical Information Games
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An overview on deep learning-based approximation methods for partial differential equations
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Data-Driven Deep Learning of Partial Differential Equations in Modal Space
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Global Optimality in Tensor Factorization, Deep Learning, and Beyond
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Rethinking floating point for deep learning
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SwitchNet: a neural network model for forward and inverse scattering problems
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Parallel Tensor Compression for Large-Scale Scientific Data
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