arXiv AI

Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques

arXiv:2606. 11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism.

arXiv Machine Learning

Jun 26

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

arXiv:2606. 26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs).

By Yuanzhe Wang, Alexandre M. Tartakovsky

diffusion

arXiv Machine Learning

Jun 19

Flow Matching for Efficient and Scalable Data Assimilation

arXiv:2508. 13313v4 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations.

By Taos Transue, Bohan Chen, So Takao, Bao Wang

diffusion benchmarks

arXiv AI

Jul 20

Energy-based Transport for Amortized Bayesian Inference

arXiv:2605. 15407v3 Announce Type: replace-cross Abstract: We consider amortized Bayesian inference for nonlinear inverse problems using only samples from the joint distribution of parameters and observations, including problems with unknown functions in a Banach space.

By Ricardo Baptista, Hojjat Kaveh, Andrew M. Stuart

arXiv Machine Learning

Jul 2

Generative Model Proposal based Particle Filtering for Data Assimilation

arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.

By Chandni Nagda, Mayank Shrivastavam Gudrun Thorkelsdottir, Gan Zhang, Morteza Mardani, Arindam Banerjee

arXiv Machine Learning

Jul 9

Bifidelity Parameter Estimation Using Conditional Diffusion Models

arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.

By Caroline Tatsuoka, Minglei Yang, Dongbin Xiu, Guannan Zhang

diffusion multimodal

arXiv AI

Aug 5

Multimodal Auto-regressive Transformer Surrogate for Modeling Variable Operations and Quantifying Uncertainty in Geological Carbon Storage

arXiv:2608. 02629v1 Announce Type: cross Abstract: The use of variable well perforation and injection strategies can improve the efficiency of geological carbon storage operations.

By Yifu Han, Louis J. Durlofsky

llms multimodal

arXiv Machine Learning

Aug 4

ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection

arXiv:2608. 01793v1 Announce Type: new Abstract: Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples.

By Camile Lendering, Erkut Akdag, Joaqu\'in Figueira, Egor Bondarev

rag diffusion benchmarks

arXiv Machine Learning

Jun 10

Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.

By Ludvig Doeser, Jens Jasche

arXiv Machine Learning

Jul 23

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes

arXiv:2601. 21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.

By Louis Grenioux, Maxence Noble

diffusion multimodal

arXiv Machine Learning

Jun 9

ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing

arXiv:2605. 14285v2 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and climate science.

By Yixuan Jia, Siyi Chen, Yida Pan, Xiao Li, Lianghe Shi, Chanyong Jung, Haijie Yuan, Ismail Alkhouri, Yue Cynthia Wu, Saiprasad Ravishankar, Jeffrey A Fessler, Qing Qu

diffusion benchmarks

arXiv Machine Learning

Jul 15

Ensemble Controlled-Flow Filtering for Implicit Data Assimilation

arXiv:2607. 12975v1 Announce Type: cross Abstract: Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations.

By Zhuoyuan Li, Yue Zhao, Ming Li

multimodal

arXiv Machine Learning

Jul 2

TRIE: An Evaluation Framework for Stochastic PDE Surrogates

arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.

By Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar, Charles D. Young

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