archive
Every paper Pith has read. Search by title, abstract, or pith.
1525 papers in math.NA · page 6
-
Core-halo split removes bias in decentralized fixed-point solving
Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems
-
Bayesian PINNs contract to PDE solutions at near-minimax rates
Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
-
Localized iteration and splitting stabilize multiscale parabolic simulations
Decoupling scales via localized subspace iteration and temporal splitting for multiscale parabolic equations
-
Finite point evaluations reconstruct convex Lipschitz functionals
Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
-
Contact constraints split PDE parameter recovery into unique and non-unique cases
On a PDE-based material parameter identification problem with contact constraints
-
RQMC in walk-on-spheres beats Monte Carlo variance rates
Randomized quasi-Monte Carlo for walk on spheres
-
Transformers run preconditioned iteration for in-context kernel regression
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
-
Transformers run preconditioned iteration to solve kernel regression
Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
-
Augmented Krylov subspace jointly approximates data fit and log-det for FIR kernels
Kernel-based linear system identification using augmented Krylov subspaces
-
Convex limiter preserves invariant domains for high-order conservation schemes
Invariant domain preserving limiting of time explicit and time implicit discretizations for systems of conservation laws
-
Runge-Kutta Langevin method hits O(d^{3/2}h^{3/2}) rate without log-concavity
Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity
-
Attention beats Fourier for PDEs on irregular shapes
When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
-
Neural POD approximation cuts Krylov iterations below AMG
NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
-
NSPOD preconditioner cuts Krylov iterations below algebraic multigrid
NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
-
Neural operators beat MLPs at function interpolation using fewer parameters
Neural Operators as Efficient Function Interpolators
-
Standard formats suboptimal for vector directions
Direction-Preserving Number Representations
-
New CG-DG schemes conserve hyperbolic laws pointwise on any volume
On structure-preserving and pointwise conservative continuous DG schemes for hyperbolic systems
-
Numerical schemes preserve 1/ε entropy scaling for conservation laws
Kolmogorov $\varepsilon$-entropy of numerical solutions for scalar conservation laws with convex flux
-
Spanning-tree gauge makes Newton method work for graph optimal transport
Newton's method for optimal transport problem on graphs
-
QuadNorm removes resolution dependence in neural operators
QuadNorm: Resolution-Robust Normalization for Neural Operators
-
Neural operator cuts iterations for convolution equations
Solving Convolution-type Integral Equations using Preconditioned Neural Operators
-
Eulerian scheme converges for VPBGK plasma model
Convergence of an Eulerian scheme for the Vlasov-Poisson-BGK model
-
TREA accelerator reduces edge detection latency up to 9x
TREA: Low-precision Time-Multiplexed, Resource-Efficient Edge Accelerator for Object Detection and Classification
-
Sparse RFNNs with sSVD tackle stiff ODEs efficiently
Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
-
Rational functions reveal Kolmogorov terms by inspection
Variable decoupling and the Kolmogorov Superposition Theorem for rational functions
-
Symplectic method reduces quantum models while preserving physics
Symplectic H2 Model Reduction for High-Dimensional Linear Quantum Systems
-
Triangle method recovers camera positions from noisy directions
TriP: A Triangle Puzzle Approach to Robust Translation Averaging
-
Stochastic column-block method solves sparse nonlinear systems
On a stochastic column-block bregman method for nonlinear systems
-
Neural shift operators stabilize HJB policy evaluation
Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
-
Proximal method enforces exact isometry at mesh cell barycenters
Proximal Galerkin for the isometry constraint
-
Neural embedding slows ODE dynamics for 20x fewer steps
Accelerating the Simulation of Ordinary Differential Equations Through Physics-Preserving Neural Networks
-
Fewer order conditions yield efficient symplectic integrators for cubic and quartic forces
Efficient symplectic integrators for cubic and quartic potentials
-
Posit engine cuts ADAS power by 72 percent with near full accuracy
EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration
-
Neural operators approximate conditioning for any joint density
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
-
Neural operator approximates conditioning for any joint density
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
-
Christoffel function places sensors optimally for non-Gaussian signals
Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
-
FFT interpolation solves minimal geometry problems without matrix inversion
Solving Minimal Problems Without Matrix Inversion Using FFT-Based Interpolation
-
Tensor model reconstructs visco-plastic flows without integration
Reduced-Order Modeling of Parameterized Visco-Plastic Shallow Flows
-
Polar factor yields retraction with closed-form inverse on symplectic Stiefel
A polar-factor retraction on the symplectic Stiefel manifold with closed-form inverse
-
Low-rank kernel operator learned once and reused for all option exercise dates
Low-rank kernel methods for American option pricing
-
Recycled subspaces solve imaging problems with uncertain geometry
Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
-
Bulk harmonic extension bounds CutFEM condition number independent of cut size
Stabilization and Operator Preconditioning of Bulk--Surface CutFEM via Harmonic Extension
-
Neural solver tackles high-dimensional PIDEs with single-jump sampling
INEUS: Iterative Neural Solver for High-Dimensional PIDEs
-
Complex composition raises BDF order by one and stability to eight
Error estimation for numerical approximations of ODEs via composition techniques. Part II: BDF methods
-
Bi-Lipschitz flows give L1-universal approximation of densities
Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
-
Lower bounds on beam buckling loads from known interpolation constants
Two-sided eigenvalue bounds for the Euler-Bernoulli beam
-
Sampler cuts diffusion steps fivefold with better image quality
DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation
-
Bifocusing fails at 180-degree bistatic angle
Mathematical and experimental validation of the bifocusing method tailored for bistatic measurement
-
Method finds optimizers for conditional stochastic problems
Unbiased Gradients for a Class of Conditional Stochastic Optimization Problems
-
Combined boundary and interior mapping improves planar shape analysis
Planar morphometry via functional shape data analysis and quasi-conformal mappings