2 citations · 6 across the 11 of their papers we have counts for
12 papers
Shifted convolutions of divisor functions
Miguel N. Walsh
We establish the leading asymptotic for the shifted correlations of divisor functions.
Dictionary learning for Kernel EDMD
Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi +2
Studying nonlinear dynamical systems through their state space behavior can be challenging, and one possible alternative is to analyze them via their associated Koopman operator. T…
On the algebra of Koopman eigenfunctions and on some of their infinities
Zahra Monfared, Saksham Malhotra, Sekiya Hajime +2
For continuous-time dynamical systems with reversible trajectories, the nowhere-vanishing eigenfunctions of the Koopman operator of the system form a multiplicative group. Here, we…
Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures
Sucheta Ghosh, Felix Dietrich, Zahra Monfared
We introduce a two-stage multitask learning framework for analyzing Electroencephalography (EEG) signals that integrates denoising, dynamical modeling, and representation learning.…
Random Feature Spiking Neural Networks
Maximilian Gollwitzer, Felix Dietrich
Spiking Neural Networks (SNNs) as Machine Learning (ML) models have recently received a lot of attention as a potentially more energy-efficient alternative to conventional Artifici…
Rapid training of Hamiltonian graph networks using random features
Atamert Rahma, Chinmay Datar, Ana Cukarska +1
Learning dynamical systems that respect physical symmetries and constraints remains a fundamental challenge in data-driven modeling. Integrating physical laws with graph neural net…
A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for High-dimension Shape Generation
Jiajie Fan, Amal Trigui, Andrea Bonfanti +3
Recent advancements in learning latent codes derived from high-dimensional shapes have demonstrated impressive outcomes in 3D generative modeling. Traditionally, these approaches e…
Training Hamiltonian neural networks without backpropagation
Atamert Rahma, Chinmay Datar, Felix Dietrich
Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network p…
Koopman-informed recurrent neural networks
Erik Lien Bolager, Ana Čukarska, Iryna Burak +2
Recurrent neural networks are a successful neural architecture for many time-dependent problems, including time series analysis, forecasting, and modeling of dynamical systems. In…
Accelerating Full Waveform Inversion By Transfer Learning
Divya Shyam Singh, Leon Herrmann, Qing Sun +3
Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizin…
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra +6
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a p…
Systematic construction of continuous-time neural networks for linear dynamical systems
Chinmay Datar, Adwait Datar, Felix Dietrich +1
Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation thr…