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The direct simulation Monte Carlo (DSMC) method is widely used to describe rarefied gas flows. The DSMC method accounts for the transport and collisions of computational particles, resulting in higher computational costs in the continuum…

Computational Physics · Physics 2025-08-11 Joonbeom Kim , Eunji Jun

In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort. First, we adapt the reservoir input mask to…

Emerging Technologies · Computer Science 2018-10-31 Bogdan Penkovsky , Laurent Larger , Daniel Brunner

Studying the propagation of uncertainties in a nonlinear dynamical system usually involves generating a set of samples in the stochastic parameter space and then repeated simulations with different sampled parameters. The main difficulty…

Numerical Analysis · Mathematics 2017-09-19 Nan Jiang , Michael Schneier

The validation, verification, and uncertainty quantification of computationally expensive theoretical models of quantum many-body systems require the construction of fast and accurate emulators. In this work, we develop emulators for…

We present Frequency Marching, FM, an algorithm that refines three-dimensional electron density distributions from solution X-ray scattering data in both the small- and wide-angle regimes. This algorithm is based on a series of optimization…

Biological Physics · Physics 2020-12-25 Yen-Lin Chen , Lois Pollack

We present a family of fast and accurate Dijkstra-like solvers for the eikonal equation and factored eikonal equation which compute solutions on a regular grid by solving local variational minimization problems. Our methods converge…

Numerical Analysis · Mathematics 2019-09-06 Samuel F. Potter , Maria K. Cameron

Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency for long sequences. Diffusion Language Models (DLMs)…

Computation and Language · Computer Science 2026-04-14 Amin Karimi Monsefi , Nikhil Bhendawade , Manuel Rafael Ciosici , Dominic Culver , Yizhe Zhang , Irina Belousova

Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains a fundamental…

Machine Learning · Computer Science 2026-05-27 Jiahe Huang , Sihan Xu , Sharvaree Vadgama , Rose Yu

The kernel-independent fast multipole method (KIFMM) proposed in [1] is of almost linear complexity. In the original KIFMM the time-consuming M2L translations are accelerated by FFT. However, when more equivalent points are used to achieve…

Numerical Analysis · Computer Science 2015-03-19 Yanchuang Cao , Lihua Wen , Junjie Rong

A delayed feedback reservoir (DFR) is a reservoir computing system well-suited for hardware implementations. However, achieving high accuracy in DFRs depends heavily on selecting appropriate hyperparameters. Conventionally, due to the…

Hardware Architecture · Computer Science 2025-04-18 Sosei Ikeda , Hiromitsu Awano , Takashi Sato

The Front-Tracking Method (FTM) is a promising approach for numerical solution of multiphase flows, considering a trade-off between accuracy and computational cost. The existing open-source open-access software for FTM is scarce, due to…

Fluid Dynamics · Physics 2026-02-25 Ehsan Amani

The hierarchical equations of motion (HEOM) approach is an accurate method to simulate open system quantum dynamics, which allows for systematic convergence to numerically exact results. To represent the effects of the bath, the reservoir…

Strongly Correlated Electrons · Physics 2023-05-31 Xiaohan Dan , Meng Xu , J. T. Stockburger , J. Ankerhold , Qiang Shi

Stable, accurate, divergence-free simulation of magnetized supersonic turbulence is a severe test of numerical MHD schemes and has been surprisingly difficult to achieve due to the range of flow conditions present. Here we present a new,…

Astrophysics of Galaxies · Physics 2012-02-20 Sergey D. Ustyugov , Mikhail V. Popov , Alexei G. Kritsuk , Michael L. Norman

Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradient-based training and memory bottlenecks. Reservoir Computing…

Machine Learning · Computer Science 2026-01-05 Ata Akbari Asanjan , Filip Wudarski , Daniel O'Connor , Shaun Geaney , Elena Strbac , P. Aaron Lott , Davide Venturelli

Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-constrained spacecraft. However, a key shortcoming in current…

Robotics · Computer Science 2025-01-03 Julia Briden , Breanna Johnson , Richard Linares , Abhishek Cauligi

We present a method for pursuit/evasion that is highly efficient and and scales to large teams of aircraft. The underlying algorithm is an efficient algorithm for solving Markov Decision Processes (MDPs) that supports fully continuous state…

Artificial Intelligence · Computer Science 2019-09-11 Joshua R. Bertram , Peng Wei

Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. We propose Interpolative Multi-Marginal Flow Matching…

Background: The computation of the statistical properties of motif occurrences has an obviously relevant practical application: for example, patterns that are significantly over- or under-represented in the genome are interesting candidates…

Genomics · Quantitative Biology 2021-11-01 Paolo Ribeca , Emanuele Raineri

The Fast Multipole Method (FMM) offers an acceleration for pairwise interaction calculation, known as $N$-body problems, from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$ with $N$ particles. This has brought dramatic increase in the capability of…

Data Structures and Algorithms · Computer Science 2011-09-21 Felipe A. Cruz , L. A. Barba

Branching processes are a class of continuous-time Markov chains (CTMCs) prevalent for modeling stochastic population dynamics in ecology, biology, epidemiology, and many other fields. The transient or finite-time behavior of these systems…

Computation · Statistics 2023-02-24 Achal Awasthi , Jason Xu