RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
Trefethen.Approximation Theory and Approximation Practice
7 Pith papers cite this work, alongside 963 external citations. Polarity classification is still indexing.
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Chebyshev polynomial approximations with adaptive sampling solve canonical differential equations for Feynman integrals, demonstrated to be stable and competitive for two-loop five-point cases in double precision.
A general framework for iterative contour integral-based methods for nonlinear eigenvalue problems is introduced, enabling a proof of linear convergence for NLFEAST under mild assumptions.
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
CHESS package implements Chebyshev-Lobatto spectral collocation for transporting epsilon-factorized differential equations of Feynman master integrals with benchmarks showing rapid convergence and shorter wall times than local series methods.
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
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Chebyshev Approximations of Feynman Integrals for Collider Physics
Chebyshev polynomial approximations with adaptive sampling solve canonical differential equations for Feynman integrals, demonstrated to be stable and competitive for two-loop five-point cases in double precision.
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Linear convergence of iterative contour integral-based eigensolvers for nonlinear eigenvalue problems
A general framework for iterative contour integral-based methods for nonlinear eigenvalue problems is introduced, enabling a proof of linear convergence for NLFEAST under mild assumptions.
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Encrypted Neural Networks without Overflows
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
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CHESS: CHEbyshev pSeudo-Spectral transport for Feynman integral differential equations
CHESS package implements Chebyshev-Lobatto spectral collocation for transporting epsilon-factorized differential equations of Feynman master integrals with benchmarks showing rapid convergence and shorter wall times than local series methods.
- Programming with Chebfun. Case study: Richards equation