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Data analysis and interpretation often relies on an approximation of an empirical dataset by some analytic functions or models. Actual implementations usually rely on a non-linear multi-dimensional optimization algorithm, typically…

Instrumentation and Methods for Astrophysics · Physics 2025-01-29 Igor Chilingarian , Kirill Grishin

The dynamic range of our normal life can exceeds 120 dB, however, the smart-phone cameras and the conventional digital cameras can only capture a dynamic range of 90 dB, which sometimes leads to loss of details for the recorded image. Now,…

Image and Video Processing · Electrical Eng. & Systems 2021-01-11 Ziyi Liu

In recent years, large-scale numerical simulations played an essential role in estimating the effects of explosion events in urban environments, for the purpose of ensuring the security and safety of cities. Such simulations are…

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two…

Computer Vision and Pattern Recognition · Computer Science 2022-10-05 Yasuhiro Yao , Ryoichi Ishikawa , Shingo Ando , Kana Kurata , Naoki Ito , Jun Shimamura , Takeshi Oishi

In aircraft design, structural optimization and uncertainty quantification concerning transonic aeroelastic issues are computationally impractical, because the iterative process requires great number of aeroelastic analysis. Emerging…

Fluid Dynamics · Physics 2018-08-15 Ziyi Wang , Weiwei Zhang , Xiaojing Wu , Kongjin Chen

Parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) have become the dominant paradigm for adapting large pretrained models. We present a theoretical framework explaining an underexplored property: LoRA's inherent…

Machine Learning · Computer Science 2026-02-03 Brady Steele

Multiple model reduction techniques have been proposed to tackle linear and non linear problems. Intrusive model order reduction techniques exhibit high accuracy levels, however, they are rarely used as a standalone industrial tool, because…

Computational Engineering, Finance, and Science · Computer Science 2025-04-10 Mikhael Tannous , Chady Ghnatios , Eivind Fonn , Trond Kvamsdal , Francisco Chinesta

Motivation: Seed filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. Read mappers 1) quickly…

In LiDAR sensing, glass, mirrors and other material often cause inconsistent data readings, because the laser beams may report the distance of the glass, the distance of the object behind the glass or the distance to a reflected object.…

Robotics · Computer Science 2024-06-18 Yinjie Li , Xiting Zhao , Sören Schwertfeger

A novel method for the numerical prediction of the slowly varying dynamics of nonlinear mechanical systems has been developed. The method is restricted to the regime of an isolated nonlinear mode and consists of a two-step procedure: In the…

Computational Engineering, Finance, and Science · Computer Science 2021-01-01 Malte Krack , Lars Panning-von Scheidt , Jörg Wallaschek

An advanced non-linear cascading filter algorithm for the removal of high density salt and pepper noise from the digital images is proposed. The proposed method consists of two stages. The first stage Decision base Median Filter (DMF) acts…

Computer Vision and Pattern Recognition · Computer Science 2015-04-14 Arabinda Dash , Sujaya Kumar Sathua

A novel reduced order model (ROM) for incompressible flows is developed by performing a Galerkin projection based on a fully (space and time) discrete full order model (FOM) formulation. This 'discretize-then-project' approach requires no…

The paper introduces a reduced order model (ROM) for numerical integration of a dynamical system which depends on multiple parameters. The ROM is a projection of the dynamical system on a low dimensional space that is both problem-dependent…

Numerical Analysis · Mathematics 2022-06-08 Alexander V. Mamonov , Maxim A. Olshanskii

Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to…

Applications · Statistics 2026-03-12 Dongeek Shin , Ahmed Kirmani , Vivek K Goyal , Jeffrey H. Shapiro

As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and…

Robotics · Computer Science 2023-03-02 Jeff Ford , Jordan Ford

Many modern ultrasound beamformers report improved image quality when evaluated using classical criteria like the contrast ratio and contrast-to-noise ratio, which are based on summary statistics of regions of interest (ROIs). However,…

Medical Physics · Physics 2026-02-03 Dongwoon Hyun

A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can be…

Instrumentation and Methods for Astrophysics · Physics 2025-09-15 Yashil Sukurdeep , Tamás Budavári , Andrew J. Connolly , Fausto Navarro

Three non-equilibrium photoionisation algorithms for hydrodynamical grid-based simulation codes are compared in terms of accuracy, timestepping criteria, and parallel scaling. Explicit methods with first-order time accuracy for photon…

Astrophysics of Galaxies · Physics 2012-03-09 Jonathan Mackey

We introduce a novel approach to waveform inversion, based on a data driven reduced order model (ROM) of the wave operator. The presentation is for the acoustic wave equation, but the approach can be extended to elastic or electromagnetic…

Numerical Analysis · Mathematics 2022-12-29 Liliana Borcea , Josselin Garnier , Alexander V. Mamonov , Jörn Zimmerling

Time-dependent partial differential equations are ubiquitous in physics-based modeling, but they remain computationally intensive in many-query scenarios, such as real-time forecasting, optimal control, and uncertainty quantification.…

Machine Learning · Computer Science 2026-01-26 Sven Dummer , Dongwei Ye , Christoph Brune
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