NEW ISSUE ALERT: Vol. 15, Issue 6 International Journal for Uncertainty Quantification is out! Multigrid meets Monte Carlo, Bayesian calibration for unseen outputs & operator learning that quantifies its own errors. https://t.co/tIfQhapOQL #UncertaintyQuantification

NEW ISSUE ALERT: Vol. 15, Issue 6 International Journal for Uncertainty Quantification is out! Multigrid meets Monte Carlo, Bayesian calibration for unseen outputs & operator learning that quantifies its own errors. https://t.co/fODbhrrdgP #UncertaintyQuantification

On Singular Bayesian Inference of Underdetermined Quantities—Part I: Invariant Discrete Ill-Posed Inverse Problems in Small and Large Dimensions https://t.co/4ddY4PvCFa By Fabrice Pautot From the MaxEnt 2024 #BayesianInference #UncertaintyQuantification @MdpiPhysci

On the SIAM News blog, Lorenzo Tamellini, Yueting Li, Chiara Piazzola, and Claudia Zoccarato utilize #uncertaintyquantification to help with land subsidence and improve groundwater exploitation policy in the Guadalentín Basin aquifer. Read more! #SIAMCSE25 https://t.co/gwogrWRY3B

📚 A Framework for #MultiPhysics Modeling, Design Optimization and #UncertaintyQuantification of Fast-Spectrum Liquid-Fueled #MoltenSalt #Reactors 🔗 https://t.co/FnqgFkovqg 👨‍🔬 by David Holler et al. 🏫 @NCState #sensitivityanalysis #CFD

Conformal Approach to Gaussian Process Surrogate Evaluation with Marginal Coverage Guarantees https://t.co/U38rR8z9dW #MachineLearning #UncertaintyQuantification #BayesianMachineLearning

Concurrent, Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks https://t.co/Let2yx1ukE #SteinVariationalInference #BayesianNeuralNetworks #UncertaintyQuantification

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation https://t.co/sJSpHlFyen #UncertaintyQuantification #NormalizingFlows #MachineLearningForScience

It's happening! Glad to be participating in ESREL 2024 happening now at the Jagiellonian University, Faculty of Mathematics and Informatics. Here We Go! 🇵🇱 #ESREL #Krakow #Poland #Risk #Reliability #UncertaintyQuantification

Our PhD student Priyanka presented her research work on #UncertaintyQuantification of complex thin-walled #composites which has attracted a significant interest, in the #ICCMS conference held recently at #IITMandi #womeninSTEM

CASC R&D group highlight: The UQ Optimization Group researches at the intersection of #Math, #ComputerScience, & applied science & engineering to enable large-scale optimization, control, & #UncertaintyQuantification for complex systems. https://t.co/d8S5NGkcFA #computing

Agenda now published - take a look and secure your place: https://t.co/wvQirhTHMh @UKAEAofficial #experimentalmechanics #digitaltwin #uncertaintyquantification

It's happening! Glad to be participating in ESREL 2023 happening now at the University of Southampton! Excited to be back in the UK! Here We Go! @esrel2023 #ESREL #Risk #Reliability #UncertaintyQuantification

Solving #UncertaintyQuantification problems on realistic models can be computationally challenging. In SIAM News, Linus Seelinger and Anne Reinarz introduce UM-Bridge: an interface that provides a link between any UQ code and model #software. Read more: https://t.co/WO3COUReFs

Validation and uncertainty quantification for digital twins workshop at the UK Atomic Energy Authority in June: bringing together computational and experimental mechanics communities. https://t.co/wvQirhTHMh #digitaltwin #uncertaintyquantification @UKAEAofficial

International Journal for Uncertainty Quantification achieved 1.8 impact factor in Clarivate's 2025 JCR (+0.3 from last year). Thanks to our editorial board, authors & reviewers! #ImpactFactor #ScholComm #Research #UncertaintyQuantification #JCR2025

Addressing the challenges and introducing a new approach to manage uncertainty in long-text generation by LLMs. #UncertaintyQuantification #LLMs #Longtextgeneration

Bayesian methods quantify uncertainty in predictions, not just point estimates. Knowing confidence matters as much as knowing predictions. Probabilistic intelligence. #MachineLearningAlgorithms #BayesianML #UncertaintyQuantification

#BayesDL #BayesLLM #UncertaintyQuantification #NeurIPS2025 Bayesian LLMs have long been difficult to deploy efficiently and reliably, due to heavy training requirements and high inference costs.