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Implicit particle filtering is a sequential Monte Carlo method for data assim- ilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by min- imizing, for…

Numerical Analysis · Mathematics 2015-05-30 Matthias Morzfeld , Alexandre J. Chorin

Smoothing short-wavelength charge density variations can stabilize explicit electrostatic particle-in-cell (PIC) plasma simulations against grid heating and cold beam instabilities, which cause unphysical heating when the Debye length is…

Plasma Physics · Physics 2025-03-10 Gregory R. Werner , Luke C. Adams , John R. Cary

Diffusion models that can generate high-quality data from randomly sampled Gaussian noises have become the mainstream generative method in both academia and industry. Are randomly sampled Gaussian noises equally good for diffusion models?…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Zipeng Qi , Lichen Bai , Haoyi Xiong , Zeke Xie

Variational quantum dynamics simulations (VQDS) provide a promising route to simulate real- and imaginary-time quantum dynamics on noisy intermediate-scale quantum devices using fixed-depth circuits. However, their practical performance is…

Quantum Physics · Physics 2026-05-21 Feng Zhang , Niladri Gomes , Joshua Aftergood , Thomas Iadecola , Yong-Xin Yao , Peter P. Orth

I compare various popular and unpopular techniques for simulating large collisionless stellar systems. I give a quantitative comparison of the raw cpu times required for five separate codes, including tree codes and basis function…

Astrophysics · Physics 2008-02-03 J A Sellwood

Stratified sampling is a fast and simple method to generate point sets with uniform distribution in hypercubes. However, for the most common paraxial stratfication it has the prominent drawback that the number of sampled points in n…

Computation · Statistics 2018-06-14 Simon Wessing

We investigate the problem of designing optimal classifiers in the strategic classification setting, where the classification is part of a game in which players can modify their features to attain a favorable classification outcome (while…

Machine Learning · Computer Science 2020-05-19 Mark Braverman , Sumegha Garg

"Particle methods" are sequential Monte Carlo algorithms, typically involving importance sampling, that are used to estimate and sample from joint and marginal densities from a collection of a, presumably increasing, number of random…

Computation · Statistics 2014-07-17 J. N. Corcoran , D. Jennings

Noise is often regarded as a limitation of quantum computers. In this work, we show that in the dynamical mean field theory (DMFT) approach to strongly-correlated systems, it can actually be harnessed to our advantage. Indeed, DMFT maps a…

Quantum Physics · Physics 2025-08-01 Corentin Bertrand , Pauline Besserve , Michel Ferrero , Thomas Ayral

Denoising Diffusion Probabilistic Models (DDPMs) have gained great attention in adversarial purification. Current diffusion-based works focus on designing effective condition-guided mechanisms while ignoring a fundamental problem, i.e., the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Jiancheng Zhang , Peiran Dong , Yongyong Chen , Yin-Ping Zhao , Song Guo

We present a set of methods to generate less complex error channels by quantum circuit parallelisation. The resulting errors are simplified as a consequence of their symmetrisation and randomisation. Initially, the case of a single error…

Quantum Physics · Physics 2023-05-26 James Mills , Debasis Sadhukhan , Elham Kashefi

We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and if necessary, adjust the residual norm of…

Atmospheric and Oceanic Physics · Physics 2013-06-03 Xiaodong Luo , Ibrahim Hoteit

In recent years, the paradigm of cloud computing has emerged as an architecture for computing that makes use of distributed (networked) computing resources. In this paper, we consider a distributed computing algorithmic scheme for…

Optimization and Control · Mathematics 2017-09-22 Shi Pu , Alfredo Garcia

The long term aim is to use modern dynamical systems theory to derive discretisations of noisy, dissipative partial differential equations. As a first step we here consider a small domain and apply stochastic centre manifold techniques to…

Dynamical Systems · Mathematics 2025-10-20 A. J. Roberts

Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them…

Signal Processing · Electrical Eng. & Systems 2023-02-03 Fernando Gama , Nicolas Zilberstein , Martin Sevilla , Richard Baraniuk , Santiago Segarra

The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that…

To simulate noisy boson sampling approximating it by only the lower-order multi-boson interferences (e.g., by a smaller number of interfering bosons and classical particles) is very popular idea. I show that the output data from any such…

Quantum Physics · Physics 2022-04-19 Valery Shchesnovich

Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy…

Quantum Physics · Physics 2022-02-01 Christophe Piveteau , David Sutter , Stefan Woerner

We adapt and study a variance reduction approach for the homogenization of elliptic equations in divergence form. The approach, borrowed from atomistic simulations and solid-state science [von Pezold et al, Physical Review B 2010; Wei et…

Numerical Analysis · Mathematics 2015-09-07 Claude Le Bris , Frederic Legoll , William Minvielle

Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit…

Image and Video Processing · Electrical Eng. & Systems 2022-11-01 Xutao Guo , Yanwu Yang , Chenfei Ye , Shang Lu , Yang Xiang , Ting Ma