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In this work, we develop patient-specific cardiocirculatory models with the aim of building Digital Twins for hypertension. In particular, in our pathophysiology-based framework, we consider both 0D cardiocirculatory models and a 3D-0D…

Numerical Analysis · Mathematics 2025-11-27 Simone Celora , Andrea Tonini , Francesco Regazzoni , Luca Dede' , Gianfranco Parati , Alfio Quarteroni

Bayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and…

Machine Learning · Computer Science 2025-02-03 Dominik Straub , Tobias F. Niehues , Jan Peters , Constantin A. Rothkopf

In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure computational efficiency. The viscoelastic nature of the subsurface is often…

Geophysics · Physics 2020-08-26 Kenneth Muhumuza , Lassi Roininen , Janne M. J. Huttunen , Timo Lähivaara

Kinetic models provide highly accurate descriptions of plasma waves but involve complex integrals that are computationally expensive to solve. To facilitate a fluid-like treatment of the system, we propose rational approximations for both…

Plasma Physics · Physics 2025-08-13 Huasheng Xie

Wrinkling of an inextensible elastic lining of an inner-lined tube under imposed pressure is considered. A simple equation modeling the elastic properties of the lining, the pressure, and the soft-substrate forces is derived. This equation…

Soft Condensed Matter · Physics 2022-01-20 Benjamin Foster , Nicolás Verschueren , Edgar Knobloch , Leonardo Gordillo

Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model…

Machine Learning · Statistics 2018-10-30 Sébastien Marmin , Maurizio Filippone

In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based inference have boosted the sensitivity of analyses;…

High Energy Physics - Phenomenology · Physics 2026-04-23 Ezequiel Alvarez , Sean Benevedes , Manuel Szewc , Jesse Thaler

The estimation of model parameters with uncertainties from observed data is a ubiquitous inverse problem in science and engineering. In this paper, we suggest an inexpensive and easy to implement parameter estimation technique that uses a…

Machine Learning · Computer Science 2020-11-06 Ushnish Sengupta , Maximilian L. Croci , Matthew P. Juniper

Identification of model parameters in computer simulations is an important topic in computer experiments. We propose a new method, called the projected kernel calibration method, to estimate these model parameters. The proposed method is…

Methodology · Statistics 2017-05-10 Rui Tuo

The Bayesian uncertainty quantification technique has become well established in turbulence modeling over the past few years. However, it is computationally expensive to construct a globally accurate surrogate model for Bayesian inference…

Data Analysis, Statistics and Probability · Physics 2022-03-16 Fanzhi Zeng , Wei Zhang , Jinping Li , Tianxin Zhang , Chao Yan

Bayesian estimation is a powerful theoretical paradigm for the operation of quantum sensors. However, the Bayesian method for statistical inference generally suffers from demanding calibration requirements that have so far restricted its…

Quantum Physics · Physics 2021-09-22 Samuel P. Nolan , Augusto Smerzi , Luca Pezzè

Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end,…

Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. In the context of Bayesian inverse problems, Uncertainty…

Numerical Analysis · Mathematics 2025-09-16 Andrea Tonini , Tan Bui-Thanh , Francesco Regazzoni , Luca Dede' , Alfio Quarteroni

Bayesian methods are actively used for parameter identification and uncertainty quantification when solving nonlinear inverse problems with random noise. However, there are only few theoretical results justifying the Bayesian approach.…

Statistics Theory · Mathematics 2020-02-04 Vladimir Spokoiny

Modeling cardiovascular anatomies poses a significant challenge due to their complex, irregular structures and inherent pathological conditions. Numerical simulations, while accurate, are often computationally expensive, limiting their…

Machine Learning · Computer Science 2025-03-25 Navaneeth N , Souvik Chakraborty

Statistical models often include thousands of parameters. However, large models decrease the investigator's ability to interpret and communicate the estimated parameters. Reducing the dimensionality of the parameter space in the estimation…

Methodology · Statistics 2022-05-16 Eric Dunipace , Lorenzo Trippa

Bayesian inference of gravitational wave signals is subject to systematic error due to modelling uncertainty in waveform signal models, coined approximants. A growing collection of approximants are available which use different approaches…

General Relativity and Quantum Cosmology · Physics 2020-03-25 Gregory Ashton , Sebastian Khan

The recent development of compressed sensing has led to spectacular advances in the understanding of sparse linear estimation problems as well as in algorithms to solve them. It has also triggered a new wave of developments in the related…

Information Theory · Computer Science 2016-07-05 Christophe Schülke

Identifying and calibrating quantitative dynamical models for physical quantum systems is important for a variety of applications. Here we present a closed-loop Bayesian learning algorithm for estimating multiple unknown parameters in a…

This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a…

Machine Learning · Statistics 2026-02-13 Jean-François Giovannelli