Convergence rates for ansatz‐free data‐driven inference in physically constrained problems
From MaRDI portal
Publication:6188899
DOI10.1002/ZAMM.202200481arXiv2210.02846OpenAlexW4382722011MaRDI QIDQ6188899
Sergio Conti, Franca Hoffmann, Michael Ortiz
Publication date: 8 February 2024
Published in: ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2210.02846
Cites Work
- Bayes procedures for adaptive inference in inverse problems for the white noise model
- Bayesian inverse problems with Gaussian priors
- Data-driven problems in elasticity
- A nonlinear structured population model: Lipschitz continuity of measure-valued solutions with respect to model ingredients
- Hyperparameter estimation in Bayesian MAP estimation: parameterizations and consistency
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Data-driven computational mechanics
- Model-free data-driven inference in computational mechanics
- Model-free and prior-free data-driven inference in mechanics
- Inverse problems: A Bayesian perspective
- Kullback--Leibler Approximation for Probability Measures on Infinite Dimensional Spaces
- Deep neural network expression of posterior expectations in Bayesian PDE inversion
- Approximate Bayesian Computation with the Wasserstein Distance
This page was built for publication: Convergence rates for ansatz‐free data‐driven inference in physically constrained problems