# Data Driven Computing with Noisy Material Data Sets

@article{Kirchdoerfer2017DataDC, title={Data Driven Computing with Noisy Material Data Sets}, author={Trenton Kirchdoerfer and Michael Ortiz}, journal={Computer Methods in Applied Mechanics and Engineering}, year={2017}, volume={326}, pages={622-641} }

## 118 Citations

A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database

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Data-Driven Approach to Computational Mechanics via Local Robust Regression

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A data-driven approach that is robust against noise and outliers in the data set, using the nearest neighbors to reduce the influence of noise, compared with the existing method that uses a single data point.

Data-Driven Computing

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Possible method improvements discuss incorporation of data quality metrics, and adaptive data additions while new applications focus on multi-scale analysis and the need for public databases to support constitutive data collaboration.

Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach

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A simple heuristic is presented that is robust against noise and outliers in a data set, and using the nearest neighbors reduces the influence of noise, compared with the existing method that uses a single data point.

Model-Free Data-Driven Inference in Computational Mechanics

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- 2022

. We extend the model-free Data-Driven computing paradigm to solids and structures that are stochastic due to intrinsic randomness in the material behavior. The behavior of such materials is…

Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids

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Abstract As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has…

Efficient Data Structures for Model-free Data-Driven Computational Mechanics

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Model-free Data-Driven Computational Mechanics Enhanced by Tensor Voting

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- 2021

A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

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This work presents a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit and provides examples on the performance of the technique in up to ten dimensions.

Variational framework for distance-minimizing method in data-driven computational mechanics

- Mathematics, Computer ScienceComputer Methods in Applied Mechanics and Engineering
- 2020

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