#uncertainty quantification
45 papers · 1 filter
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Hubert Leterme, Andreas Tersenov, Jalal Fadili +1
The paper presents PnPMass, a plug‑and‑play algorithm that reconstructs dark‑matter maps from weak‑lensing shear data using a single deep‑learning denoiser combined with gradient d…
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
The paper examines how neural network architectures such as feedforward nets, Deep Sets, and Transformers can be used to amortize Bayesian inference, providing fast approximate pos…
Estimation of Elastic Parameters with Guidance-based Diffusion model
Anjali Dixit, Francesco Brandolin, Tariq Alkhalifah
The paper presents a guided diffusion model for inverting angle‑stack seismic data to estimate elastic parameters (P‑wave velocity, S‑wave velocity, and density), delivering sharpe…
Human population dynamics as a Bayesian inverse transport problem
Chong Qi
The paper presents a Bayesian inverse transport method that embeds Bayesian neural networks into conservation-law equations to model and predict human population movements while pr…
ANGLE: Angular Neural Generative Learning via Engression
Rajdeep Pathak, Archi Roy, Tanujit Chakraborty
The paper introduces ANGLE, a lightweight deep generative model that learns the full conditional distribution of circular (angular) data given various covariates, enabling better p…
An Extension to the Procedure for Developing Uncertainty-Consistent Shear Wave Velocity Profiles from Inversion of Experimental Surface Wave Dispersion Data
Joseph P. Vantassel, Brady R. Cox
The paper extends a previously proposed method for generating shear‑wave velocity (Vs) profiles that honor the uncertainty of surface‑wave dispersion measurements, introducing two…