Tootfinder

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

This https://arxiv.org/abs/2303.00847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of Four-Dimensional Variational Data Assimilation Problems in Low Regularity Spaces
We carry out a rigorous analysis of four-dimensional variational data assimilation ($4D$-VAR) problems for linear and semilinear parabolic partial differential equations. Continuity of the state with respect to the spatial variable is required since pointwise observations of the state variable appear in the cost functional. Using maximal parabolic regularity tools, we prove this regularity for initial conditions with $L^β$-regularity guaranteed by control constraints, rather than Sobolev regul…

An optical-lensing inspired data thinning method for nuclear cross section data
M. Imbri\v{s}ak, A. E. Lovell, M. R. Mumpower
https://arxiv.org/abs/2508.19470 https://

An optical-lensing inspired data thinning method for nuclear cross section data
In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel variant of the data thinning algorithm, inspired by the principles of optical lensing, which effectively reduces data volume while preserving critical information. We show how it improves fitting through a toy problem and for several examples of total cross sections for neutron-induced reactions on rare-earth isotopes.…

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Zekun Ni, Jonathan Weyn, Hang Zhang, Yanfei Xiang, Jiang Bian, Weixin Jin, Kit Thambiratnam, Qi Zhang, Haiyu Dong, Hongyu Sun
https://arxiv.org/abs/2508.18486

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state…

Numerical study of high-dimensional covariance estimation and localization for data assimilation
Shay Gilpin, Matthias Morzfeld, Kevin K. Lin
https://arxiv.org/abs/2508.18299 ht…

Numerical study of high-dimensional covariance estimation and localization for data assimilation
Covariance localization is a critical component of ensemble-based data assimilation (DA) and many current localization schemes simply dampen correlations as a function of distance. Increases in computational resources, broadening scope of application for DA, and advances in general statistical methodology raise the question as to whether alternative localization methods may improve ensemble DA relative to current schemes. We carefully explore this issue by comparing distance based localization …

Modular data assimilation for flow prediction
Aytekin \c{C}{\i}b{\i}k, Rui Fang, William Layton
https://arxiv.org/abs/2506.19002 https://

Modular data assimilation for flow prediction
This report develops several modular, 2-step realizations (inspired by Kalman filter algorithms) of nudging-based data assimilation \begin{equation*} \begin{array}{cc} Step{\text { }}1 & \begin{array}{c} \frac{\widetilde {v}^{n+1}-v^{n}}{k}+v^{n}\cdot \nabla \widetilde {v}% ^{n+1}-ν\triangle \widetilde {v}^{n+1}+\nabla q^{n+1}=f(x){\text { }% } \\ \nabla \cdot \widetilde {v}^{n+1}=0% \end{array} \\ Step{\text { }}2 & \frac{v^{n+1}-\widetilde {v}^{n+1}}{k}-χI_{H}(u(t^{n+1})-v^{n+1})=0.% \end{a…

Uncertain data assimilation for urban wind flow simulations with OpenLB-UQ
Mingliang Zhong, Dennis Teutscher, Adrian Kummerl\"ander, Mathias J. Krause, Martin Frank, Stephan Simonis
https://arxiv.org/abs/2508.18202

Uncertain data assimilation for urban wind flow simulations with OpenLB-UQ
Accurate prediction of urban wind flow is essential for urban planning, pedestrian safety, and environmental management. Yet, it remains challenging due to uncertain boundary conditions and the high cost of conventional CFD simulations. This paper presents the use of the modular and efficient uncertainty quantification (UQ) framework OpenLB-UQ for urban wind flow simulations. We specifically use the lattice Boltzmann method (LBM) coupled with a stochastic collocation (SC) approach based on gene…

Data assimilation with the 2D Navier-Stokes equations: Optimal Gaussian asymptotics for the posterior measure
Dimiri Konen, Richard Nickl
https://arxiv.org/abs/2507.18279 https:…

Data assimilation with the 2D Navier-Stokes equations: Optimal Gaussian asymptotics for the posterior measure
A functional Bernstein - von Mises theorem is proved for posterior measures arising in a data assimilation problem with the two-dimensional Navier-Stokes equation where a Gaussian process prior is assigned to the initial condition of the system. The posterior measure, which provides the update in the space of all trajectories arising from a discrete sample of the (deterministic) dynamics, is shown to be approximated by a Gaussian random vector field arising from the solution to a linear parabol…

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao, Bao Wang
https://arxiv.org/abs/2508.13313 https://

Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Data assimilation (DA) is the problem of sequentially estimating the state of a dynamical system from noisy observations. Recent advances in generative modeling have inspired new approaches to DA in high-dimensional nonlinear settings, especially the ensemble score filter (EnSF). However, these come at a significant computational burden due to slow sampling. In this paper, we introduce a new filtering framework based on flow matching (FM) -- called the ensemble flow filter (EnFF) -- to accelera…

Nonlinear Splitting for Gradient-Based Unconstrained and Adjoint Optimization
Brian K. Tran, Ben S. Southworth, David B. Cavender, Sam Olivier, Syed A. Shah, Tommaso Buvoli
https://arxiv.org/abs/2508.20280

Nonlinear Splitting for Gradient-Based Unconstrained and Adjoint Optimization
High dimensional and/or nonconvex optimization remains a challenging and important problem across a wide range of fields, such as machine learning, data assimilation, and partial differential equation (PDE) constrained optimization. Here we consider gradient-based methods for solving unconstrained and constrained optimization problems, and introduce the concept of nonlinear splitting to improve accuracy and efficiency. For unconstrained optimization, we consider splittings of the gradient to de…

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
Dawei Li, Henry D. I. Abarbanel
https://arxiv.org/abs/2506.11365

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timin…

Data Assimilation in Large Eddy Simulation: Addressing Model-Observation Mismatch from Navier-Stokes Data
Adam Larios, Ali Pakzad, Nicholas White
https://arxiv.org/abs/2508.07492

Data Assimilation in Large Eddy Simulation: Addressing Model-Observation Mismatch from Navier-Stokes Data
In atmospheric and turbulent flow modeling, Large Eddy Simulation (LES) is often used to reduce computational cost, while observational data typically originates from the underlying physical system. Motivated by this setting, we study a continuous data assimilation (CDA) algorithm applied to a Smagorinsky/Ladyzhenskaya-type LES model, in which the observational data is generated from the full Navier--Stokes equations (NSE). In the two-dimensional setting, we establish global well-posedness of t…

Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material
Guanjin Wang, Xiangxue Zhao, Shapour Azarm, Balakumar Balachandran
https://arxiv.org/abs/2506.10875

Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material
An alternative data-driven modeling approach has been proposed and employed to gain fundamental insights into robot motion interaction with granular terrain at certain length scales. The approach is based on an integration of dimension reduction (Sequentially Truncated Higher-Order Singular Value Decomposition), surrogate modeling (Gaussian Process), and data assimilation techniques (Reduced Order Particle Filter). This approach can be used online and is based on offline data, obtained from the…

SODA4: a mesoscale ocean/sea ice reanalysis 1980-2024
Gennady A. Chepurin, James A. Carton, Luyu Sun, Stephen G. Penny
https://arxiv.org/abs/2508.17462 https://

SODA4: a mesoscale ocean/sea ice reanalysis 1980-2024
This paper describes the new Simple Ocean Data Assimilation version 4 (SODA4) global eddy-resolving ocean/sea ice reanalysis that spans the 45-year period 1980-2024. The reanalysis is constructed using GFDL MOM5/SIS1 numerics and ECMWF ERA5 forcings with surface and subsurface temperature and salinity observations as constraints within an optimal interpolation data assimilation algorithm. The method of construction and resulting output files are described. Comparison of the SODA4 temperature an…

Multi-Scale Data Assimilation in Turbulent Models
Francesco Fossella, Luca Biferale, Alberto Carrassi, Massimo Cencini, Vikrant Gupta
https://arxiv.org/abs/2507.15626 https://…

Multi-Scale Data Assimilation in Turbulent Models
We explore the potential of Data-Assimilation (DA) within the multi-scale framework of a shell model of turbulence, with a focus on the Ensemble Kalman Filter (EnKF). The central objective is to understand how measuring mesoscales (i.e., inertial-range scales) enhances the prediction of both large-scale and small-scale intermittent variables, by systematically varying observation frequency and the set of measured scales. We demonstrate that measurements conducted at frequencies that exceed thos…

Data assimilation using a global Girsanov nudged particle filter
Maneesh Kumar Singh, Joshua Hope-Collins, Colin J. Cotter, Dan Crisan
https://arxiv.org/abs/2507.17685 https://

Data assimilation using a global Girsanov nudged particle filter
We present a particle filtering algorithm for stochastic models on infinite dimensional state space, making use of Girsanov perturbations to nudge the ensemble of particles into regions of higher likelihood. We argue that the optimal control problem needs to couple control variables for all of the particles to maintain an ensemble with good effective sample size (ESS). We provide an optimisation formulation that separates the problem into three stages, separating the nonlinearity in the ESS ter…

Combining machine learning with data assimilation to improve the quality of phytoplankton forecasting in a shelf sea environment
Deep S Banerjee, Jozef Skakala, David Ford
https://arxiv.org/abs/2508.02400

Combining machine learning with data assimilation to improve the quality of phytoplankton forecasting in a shelf sea environment
We demonstrate that combining machine learning with data assimilation leads to a major improvement in phytoplankton short-range (1-5 day) forecasts for the North-West European Shelf (NWES) seas. We show that excess nitrate concentrations are a major reason behind known biases in phytoplankton forecasts during late Spring and Summer, which can grow fast with lead time. Assimilating observations of nitrate would potentially help address this, but NWES nitrate data are typically not available in s…

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
Paul Jeanney, Ashton Hetherington, Shady E. Ahmed, David Lanceta, Susana Saiz, Jos\'e Miguel Perez, Soledad Le CLainche
https://arxiv.org/abs/2507.00539

Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
This paper presents an innovative Reduced-Order Model (ROM) for merging experimental and simulation data using Data Assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach implementing the Ensemble Kalman Filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the prop…

This https://arxiv.org/abs/2501.16642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems. Model-driven methods (e.g., Kalman, particle) presuppose full knowledge of the true dynamics, which is not always satisfied in practice, while purely data-driven solvers learn a deterministic mapping between observations and states and therefore miss the intrinsic stochasticity of real processes. Recently, score-based diffusion models learn a global diffusion prior and provide a goo…

Posterior contraction rates of computational methods for Bayesian data assimilation
Erik Burman, Mingfei Lu
https://arxiv.org/abs/2506.14685 https://

Posterior contraction rates of computational methods for Bayesian data assimilation
In this paper, we analyze posterior consistency of a Bayesian data assimilation problem under discretization. We prove convergence rates for the discrete posterior to ground truth solution under both conforming discretization and finite element discretization (usually non-conforming). The analysis is based on the coupling of asymptotics between the number of samples and the dimension of discrete spaces. In the finite element discretization, tailor-made discrete priors, instead of the discretiza…

Crosslisted article(s) found for nlin.CD. https://arxiv.org/list/nlin.CD/new
[1/1]:
- Dimensional dependence of synchronisation in turbulence: insights from data assimilation and Lyap...
Masanobu Inubushi, Colm-cille P. Caulfield

A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation
Bo\v{s}tjan Melinc, Uro\v{s} Perkan, \v{Z}iga Zaplotnik
https://arxiv.org/abs/2506.11968

A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation
Estimating background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertio-gravity modes - an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neu…

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Dau\v{z}ickait\.e, M. A. Freitag, S. G\"urol, A. S. Lawless, A. Ramage, J. A. Scott, J. M. Tabeart
https://arxiv.org/abs/2506.09211

An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of it…

A localized particle filter for geophysical data assimilation
Dan Crisan, Eliana Fausti
https://arxiv.org/abs/2507.07103 https://arxi…

A localized particle filter for geophysical data assimilation
Particle filters are computational techniques for estimating the state of dynamical systems by integrating observational data with model predictions. This work introduces a class of Localized Particle Filters (LPFs) that exploit spatial localization to reduce computational costs and mitigate particle degeneracy in high-dimensional systems. By partitioning the state space into smaller regions and performing particle weight updates and resampling separately within each region, these filters lever…

Active Digital Twins via Active Inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni, Francesco Donnarumma, Giovanni Pezzulo, Alberto Corigliano
https://arxiv.org/abs/2506.14453

Active Digital Twins via Active Inference
Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, conventional digital twins rely primarily on passive data assimilation, which limits their adaptability in uncertain and dynamic environments. This paper introduces the active digital twin paradigm, based on active inference. Active inference is a neuroscience-inspired, Bayesian framework for probabilistic reasoning and…

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He, Ashesh Chattopadhyay
https://arxiv.org/abs/2507.06479

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural…

Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks
Tom Moussie, Paolo Errante, Marcello Meldi
https://arxiv.org/abs/2507.13744

Multi-fidelity Ensemble Kalman Filter algorithms enhanced by Convolutional Neural Networks
The present research work proposes advancement for Data Assimilation strategies using Convolutional Neural Networks (CNN). More precisely, multi-fidelity and multi-level algorithms for the Ensemble Kalman Filter are enhanced by CNN tools, with the objective to reduce the discrepancy in the prediction between ensemble realizations performed with different models. The proposed methodology is assessed via the analysis of the flow through a cascade of NACA 0012 profiles for Reynolds $Re=1\,000$ and…

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation
Maryam Abdi-Oskouei, J\'er\^ome Barr\'e
https://arxiv.org/abs/2506.07321

Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation
The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations …

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation
Taos Transue, Bohan Chen, So Takao, Bao Wang

This https://arxiv.org/abs/2407.11746 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Sparse data assimilation for under-resolved large-eddy simulations
The need for accurate and fast scale-resolving simulations of fluid flows, where turbulent dispersion is a crucial physical feature, is evident. Large-eddy simulations (LES) are computationally more affordable than direct numerical simulations, but their accuracy depends on sub-grid scale models and the quality of the computational mesh. In order to compensate related errors, a data assimilation approach for LES is devised in this work. The presented method is based on variational assimilatio…

Using Diffusion Models to do Data Assimilation
Daniel Hodyss, Matthias Morzfeld
https://arxiv.org/abs/2506.02249 https://arxiv.org/pd…

Using Diffusion Models to do Data Assimilation
The recent surge in machine learning (ML) methods for geophysical modeling has raised the question of how these same ML methods might be applied to data assimilation (DA). We focus on diffusion modeling (a form of generative artificial intelligence) and on systems that can perform the entire DA, rather than on ML-based tools that are used within an otherwise conventional DA system. We explain that there are (at least) three different types of diffusion-based DA systems and we show in detail tha…

This https://arxiv.org/abs/2411.13443 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
This paper presents score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed method decomposes the assimilation process into alternating prediction and update steps, leveraging dynamic models for state prediction while incorporating observational data through score-based Langevin Monte Carlo during updates. To address challenges in posterior sampling, we introduce an annealing strategy within t…

What is Past is Prologue:
#Paleoclimate constraints on #ECS Equilibrium #ClimateSensitivity #GreenhouseGas sensitivity of Earth's #Climate
First of 3 papers this summer.
My career trajectory has been building towards using past climate archives to narrow the possibilities of future climate. But it is HARD. And computationally expensive.
The next two papers of this project will rule out some high and low sensitivity simulations using Past Climate (Proxy) archives and Data Assimilation. Those should be out in preprint in the next couple of months.
We hope this project (first to constrain earth climate sensitivity using coupled atmosphere-ocean earth system models) will be streamlined in the future and applied widely.
https://essopenarchive.org/users/921675/articles/1297441-on-the-sensitivity-of-climate-sensitivity-in-a-coupled-climate-model

This https://arxiv.org/abs/2506.03947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators
Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once diffusion-based representation of a covariance matrix operator in order to exploit inherent parallellism in the underlying problem. In this paper, we provide practical methods to apply block $α$-circulant preconditioners to the all-at-once system for the case where the main diffusion operation matrix cannot be readil…

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Xiantao Fan, Xinyang Liu, Meng Wang, Jian-Xun Wang
https://arxiv.org/abs/2505.23940

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modelin…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
Hang Fan, Lei Bai, Ben Fei, Yi Xiao, Kun Chen, Yubao Liu, Yongquan Qu, F…