English
Related papers

Related papers: Hierarchical Bayesian constitutive model selection…

200 papers

We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses separate conditionally Gaussian priors for each parameter vector and common gamma-distributed…

Machine Learning · Statistics 2024-05-27 Jan Glaubitz , Anne Gelb

This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an existing Fast Fourier Transform (FFT) approach to identify…

Applications · Statistics 2022-06-02 Omid Sedehi , Costas Papadimitriou , Lambros S. Katafygiotis

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during…

Machine Learning · Statistics 2017-11-22 Peter I. Frazier , Jialei Wang

This paper introduces a novel hierarchical Bayesian model specifically designed to address challenges in Inverse Uncertainty Quantification (IUQ) for time-dependent problems in nuclear Thermal Hydraulics (TH) systems. The unique…

Applications · Statistics 2024-03-27 Chen Wang

This paper proposes a probabilistic Bayesian formulation for system identification (ID) and estimation of nonseparable Hamiltonian systems using stochastic dynamic models. Nonseparable Hamiltonian systems arise in models from diverse…

Dynamical Systems · Mathematics 2022-09-19 Harsh Sharma , Nicholas Galioto , Alex A. Gorodetsky , Boris Kramer

While the performance of machine learning systems has experienced significant improvement in recent years, relatively little attention has been paid to the fundamental question: to what extent can we improve our models? This paper provides…

Machine Learning · Computer Science 2026-05-13 Ryota Ushio , Takashi Ishida , Masashi Sugiyama

Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered…

Materials Science · Physics 2023-02-14 Andrew Ma , Yang Zhang , Thomas Christensen , Hoi Chun Po , Li Jing , Liang Fu , Marin Soljačić

This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to…

Machine Learning · Computer Science 2023-05-23 Abhishek Chandra , Bram Daniels , Mitrofan Curti , Koen Tiels , Elena A. Lomonova , Daniel M. Tartakovsky

The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper, we propose a physics-informed deep-learning framework for…

Machine Learning · Computer Science 2022-04-01 Craig M. Hamel , Kevin N. Long , Sharlotte L. B. Kramer

The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central to this progress is uncovering scientific laws from experimental data through symbolic…

Methodology · Statistics 2025-09-25 Somjit Roy , Pritam Dey , Debdeep Pati , Bani K. Mallick

This thesis responds to the challenges of using a large number, such as thousands, of features in regression and classification problems. There are two situations where such high dimensional features arise. One is when high dimensional…

Machine Learning · Statistics 2007-09-20 Longhai Li

We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm…

Methodology · Statistics 2020-06-19 Youngseok Kim , Chao Gao

We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single…

Computational Engineering, Finance, and Science · Computer Science 2017-01-17 Hussein Rappel , Lars A. A. Beex , Jack S. Hale , Stephane P. A. Bordas

A fully Bayesian approach is proposed for ultrahigh-dimensional nonparametric additive models in which the number of additive components may be larger than the sample size, though ideally the true model is believed to include only a small…

Methodology · Statistics 2013-09-24 Zuofeng Shang , Ping Li

It is an important task to model realized volatilities for high-frequency data in finance and economics and, as arguably the most popular model, the heterogeneous autoregressive (HAR) model has dominated the applications in this area.…

Methodology · Statistics 2023-03-07 Huiling Yuan , Kexin Lu , Yifeng Guo , Guodong Li

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity…

Methodology · Statistics 2023-02-17 Patrícia Martinková , František Bartoš , Marek Brabec

First principles microphysics models are essential to the design and analysis of high energy density physics experiments. Using experimental data to investigate the underlying physics is also essential, particularly when simulations and…

Plasma Physics · Physics 2013-06-04 Jim A Gaffney , Dan Clark , Vijay Sonnad , Stephen B Libby

Model selection is an indispensable part of data analysis dealing very frequently with fitting and prediction purposes. In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix…

Signal Processing · Electrical Eng. & Systems 2022-09-19 Prakash B. Gohain , Magnus Jansson

When characterizing materials, it can be important to not only predict their mechanical properties, but also to estimate the probability distribution of these properties across a set of samples. Constitutive neural networks allow for the…

Computational Engineering, Finance, and Science · Computer Science 2025-03-18 Jeremy A. McCulloch , Ellen Kuhl

Magnetic Resonance Elastography allows noninvasive visualization of tissue mechanical properties by measuring the displacements resulting from applied stresses, and fitting a mechanical model. Poroelasticity naturally lends itself to…

‹ Prev 1 4 5 6 7 8 10 Next ›