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Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
Rong Jin, Guangyao Wang, Xingsheng Sun
https://arxiv.org/abs/2510.09703 https://

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
High-fidelity simulations are essential for understanding and predicting the behavior of materials under high-velocity impact (HVI) in both fundamental research and practical applications. However, their accuracy relies on material models and parameters that are traditionally obtained through manual fitting to multiple time- and labor-intensive experiments. This study presents an ensemble-based data assimilation (DA) framework to automatically and simultaneously calibrate plasticity, fracture, …

Crosslisted article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
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- Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
Rong Jin, Guangyao Wang, Xingsheng Sun

Structurally informed data assimilation in two dimensions
Tongtong Li, Anne Gelb, Yoonsang Lee
https://arxiv.org/abs/2510.06369 https://arxiv.org/pdf/2510.…

Structurally informed data assimilation in two dimensions
Accurate data assimilation (DA) for systems with piecewise-smooth or discontinuous state variables remains a significant challenge, as conventional covariance-based ensemble Kalman filter approaches often fail to effectively balance observations and model information near sharp features. In this paper we develop a structurally informed DA framework using ensemble transform Kalman filtering (ETKF). Our approach introduces gradient-based weighting matrices constructed from finite difference stati…

On the joint observability of flow fields and particle properties from Lagrangian trajectories: evidence from neural data assimilation
Ke Zhou, Samuel J. Grauer
https://arxiv.org/abs/2510.00479

On the joint observability of flow fields and particle properties from Lagrangian trajectories: evidence from neural data assimilation
We numerically investigate the joint observability of flow states and unknown particle properties from Lagrangian particle tracking (LPT) data. LPT offers time-resolved, volumetric measurements of particle trajectories, but experimental tracks are spatially sparse, potentially noisy, and may be further complicated by inertial transport, raising the question of whether both Eulerian fields and particle characteristics can be reliably inferred. To address this, we develop a data assimilation fram…

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Wencong Yang, Haoyu Ji, Leo Lonzarich, Yalan Song, Chaopeng Shen
https://arxiv.org/abs/2510.08488

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular hourly data-driven prediction scheme is multi-time-scale long short-term memory (MTS-LSTM), yet such models face challenges in probabilistic forecasts or integrating observations when available. Diffusion artificial intelligence (AI) models represent a promising method to predict high-resolution information, e.g., hourl…

A discrete data assimilation algorithm for the reconstruction of Gray--Scott dynamics
Tsiry Avisoa Randrianasolo
https://arxiv.org/abs/2510.03972 https://a…

A discrete data assimilation algorithm for the reconstruction of Gray--Scott dynamics
The Gray--Scott model governs the interaction of two chemical species via a system of reaction-diffusion equations. Despite its simple form, it produces extremely rich patterns such as spots, stripes, waves, and labyrinths. That makes it ideal for studying emergent behavior, self-organization, and instability-driven pattern formation. It is also known for its sensitivity to poorly observed initial conditions. Using such initial conditions alone quickly leads simulations to deviate from the true…

State and Parameter Estimation for a Neural Model of Local Field Potentials
Daniele Avitabile, Gabriel J. Lord, Khadija Meddouni
https://arxiv.org/abs/2512.07842 https://arxiv.org/pdf/2512.07842 https://arxiv.org/html/2512.07842
arXiv:2512.07842v1 Announce Type: new
Abstract: The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to characterize the neural activity from the cortex of a mouse during natural sleep, captured through local field potential measurements. Our approach relies on using a discretized Wilson--Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the stimulus received by the cortex from other brain regions, while simultaneously inferring a state that aligns with the observed signal.
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A Bayesian Characterization of Ensemble Kalman Updates
Frederic J. N. Jorgensen, Youssef M. Marzouk
https://arxiv.org/abs/2510.00158 https://arxiv.org/pdf/…

A Bayesian Characterization of Ensemble Kalman Updates
The update in the Ensemble Kalman Filter (EnKF), called the Ensemble Kalman Update (EnKU), is widely used for Bayesian inference in inverse problems and data assimilation. At each filtering step, it approximates the solution to a likelihood-free Bayesian inversion from an ensemble of particles $(X_i,Y_i)\simπ$ sampled from a joint measure $π$ and an observation $y_\ast\in\mathbb{R}^m$. The posterior $π_{X|Y=y_\ast}$ is approximated by transporting $(X_i,Y_i)$ through an affine map $L^{\…

Model Training, Data Assimilation, and Forecast Experiments with a Hybrid Atmospheric Model that Incorporates Machine Learning
Dylan Elliott, Troy Arcomano, Istvan Szunyogh, Brian R. Hunt
https://arxiv.org/abs/2509.22465

Model Training, Data Assimilation, and Forecast Experiments with a Hybrid Atmospheric Model that Incorporates Machine Learning
The hybrid model combines the physics-based primitive-equations model SPEEDY with a machine learning-based (ML-based) model component, while ERA5 reanalyses provide the presumed true states of the atmosphere. Six-hourly simulated noisy observations are generated for a 30-year ML training period and a one-year testing period. These observations are assimilated with a Local Ensemble Transform Kalman Filter (LETKF), and a 10-day deterministic forecast is also started from each ensemble mean analys…

Numerical Reconstruction of Coefficients in Elliptic Equations Using Continuous Data Assimilation
Peiran Zhang
https://arxiv.org/abs/2509.16954 https://arx…

Numerical Reconstruction of Coefficients in Elliptic Equations Using Continuous Data Assimilation
We consider the numerical reconstruction of the spatially dependent conductivity coefficient and the source term in elliptic partial differential equations of in a two-dimensional convex polygonal domain, with the homogeneous Dirichlet boundary condition and given interior observation of the solution. Using data assimilation, some approximated gradients of our error functional are derived to update the reconstructed coefficients, and new $L^2$ error estimates for such minimization formulations …

Physics-Informed Field Inversion for Sparse Data Assimilation
Levent Ugur (Georgia Institute of Technology), Beckett Y. Zhou (Georgia Institute of Technology)
https://arxiv.org/abs/2509.19160