#spectral methods
21 papers match
ROSA: Metric Amplification on Noisy Graphs with Theoretical Guarantees for Amplified Spectral Distances
Ben Cardoen, Fabian Spill
The paper introduces ROSA, a robust order‑aware spectral amplification operator that strengthens graph distance metrics to more reliably detect weak, localized structural changes i…
Towards long and accurate numerical relativity waveforms of binary black holes beyond general relativity
Guillermo Lara, Harald P. Pfeiffer, Nils Deppe +9
The paper presents long, accurate numerical relativity simulations of equal‑mass, nonspinning binary black holes in shift‑symmetric scalar Gauss‑Bonnet gravity, generating waveform…
A Spectral-Domain Pseudo-Inverse Construction Method for Unitary Diagonalizable Linear Inverse Problems
Shengchang Chen
The paper proposes a spectral‑domain construction of a stable pseudo‑inverse for large linear inverse problems whose system matrix is unitary‑diagonalizable, using regularization f…
A Spectral Proof of the Hypergraph Moore Bound
Alexander Schmidhuber, Matthew B. Hastings
The paper proves Feige's hypergraph Moore bound conjecture, showing that any sufficiently dense k‑uniform hypergraph contains a small even cover, using new spectral bounds for Kiku…
SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics
Shivani Saini, Ramesh Kumar Vats, Arup Kumar Sahoo
The paper introduces SpectONet, a physics‑guided spectral deep operator network that combines DeepONet with physics‑informed constraints and nonuniform Chebyshev‑Gauss‑Lobatto sens…
Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Aristotelis Ballas, Christos Diou
The paper shows that both loss‑landscape flatness and gradient alignment are essential for multi‑distribution learning and introduces SAGE, a method that jointly optimizes these pr…
Factorized Spectral Representations for Reinforcement Learning
Junyi Wu, Dan Li
The paper introduces FaStR, a method that factorizes the transition kernel of a reinforcement learning environment as a three-way tensor using CP decomposition, learning separate e…
Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs
Tianchi Yu, Ivan Oseledets
The paper introduces Modified Spectral-Informed Neural Networks (SINNs) that combine spectral methods with physics-informed neural networks, using coefficient decay scaling and bas…
Spectral and Additive Combinatorial Methods for Cycles and Absorbing Sets in Lifted-Product Quantum LDPC Codes
Aida Abiad, Nichola Castriota
The paper develops spectral and additive combinatorial techniques to analyze short cycles and absorbing sets in lifted‑product quantum LDPC codes, providing closed‑form counts and…
Scale-conditioned structure-based closure for homogeneous turbulence: Ray-Column Interacting Particle Representation Model
Stavros C. Kassinos
The paper extends particle‑representation turbulence models by decomposing the spectral vector into orientation and radial wavenumber bands (the Ray‑Column approach) to retain scal…
Coupled by Design: Computing Kerr-Newman Quasinormal Modes with a Hybrid SpectralPINN Solver
Alexandre M. Pombo
The paper introduces a hybrid SpectralPINN solver for calculating quasinormal mode frequencies of charged Kerr‑Newman black holes by solving coupled gravitational and electromagnet…
DRIFT: Direct Reduced Fourier Transforms for Distributed Spectral Neural Operators
Sana Taghipour Anvari, David Kaeli
The paper introduces DRIFT, a GPU implementation that computes only the needed frequency modes for Fourier Neural Operators using a Distributed Truncated Spectral Transform, dramat…
An end-to-end quantum algorithm for weakly nonlinear plasma physics with superquadratic speedup
Bjorn K. Berntson, David Jennings, Matteo Lostaglio +1
The paper presents a complete quantum algorithm for simulating a weakly nonlinear kinetic plasma model, using Carleman linearization, hierarchical block encoding, and a specialized…
An Adaptive Fourier Spectral Method for the Vlasov-Poisson system
Seung Yeon Cho, Giovanni Russo
The paper introduces an adaptive Fourier spectral method with high-order time splitting to dynamically expand the wavenumber domain and avoid artificial filtering when simulating t…
Spectral Diffusion Processes
Angus Phillips, Thomas Seror, Michael Hutchinson +3
The paper introduces diffusion models for stochastic processes by representing data in a spectral domain using kernels, truncating the spectral coefficients, and modeling them with…
SPECTRA: Context-Conditioned Spectral Movement Primitives for Robot Skill Generalization
Boxuan Zhang, Sheng Liu, Chenlin Ming +1
The paper introduces Spectral Movement Primitives, a frequency‑domain approach that learns robot manipulation skills from demonstrations using low‑frequency Fourier coefficients an…
RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer
Jing Li, Yawei Luo, Xiangze Meng +3
RealSkin is a self‑supervised framework that transfers visual attributes from real photos onto synthetic 3D models by learning correspondences in a spectral domain and refining the…
Explicit fractional Laplacians and Riesz potentials of classical functions
Timon S. Gutleb, Ioannis P. A. Papadopoulos
The paper derives explicit formulas for fractional Laplacians and their inverses (Riesz potentials) applied to classical orthogonal polynomials and Bessel functions, providing conc…
The CKN inequality for spinors: symmetry and symmetry breaking
Jean Dolbeault, Maria J. Esteban, Rupert L. Frank +1
The paper studies weighted Sobolev interpolation inequalities for spinor fields, examining when optimal spinors are symmetric and when symmetry breaks, using spectral analysis tech…
From Embedding Geometry to Spectral Search: Energy Dispersion Networks For Vector Retrieval
Lorenzo Moriondo, Ilias Azizi
The paper proposes Graph Wiring, a framework that leverages spectral structure of embedding spaces for vector retrieval, and introduces Spectral Indexing, which combines geometric…
SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations
Divyavardhan Singh, Dimple Sonone, Hammad Mohammad +1
The paper introduces SPARC-Net, a physics-informed neural network architecture that combines spectral encoding, causality-aware training, and hard constraints to improve the soluti…
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