Add to ORKGThe paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://doi.org/10.1016/j.eml.2020.100659. All code necessary to reproduce these finite element simulations is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-fashion). For questions, please contact Emma Lejeune (elejeune@bu.edu).Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST - Fashion is generated by first converting the fashion MNIST bitmap images (https://github.com/zalandoresearch/fashion-mnist) to 2D heterogeneous blocks of material. Consistent w...
Mechanical metamaterials such as open- and closed-cell lattice structures, foams, composites, and so...
Micromechanical modeling of material behavior has become an accepted approach to describe the macros...
Recent advances in machine learning have unlocked new potential for innovation in engineering scienc...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The associated paper “Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Traini...
The paper “Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based...
Metamodels, or models of models, map defined model inputs to defined model outputs. Typically, metam...
More details about the data description can be found in the paper "Towards out of distribution gener...
The Mechanical MNIST Crack Path dataset contains Finite Element simulation results from phase-field ...
Figure 1: Examples produced by our data-driven finite element method. Left: A bar with heterogeneous...
La simulation des procédés thermomécaniques tels que le soudage demande une description fine des com...
This work involves the modeling and understanding of mechanical behavior of crystalline materials us...
Mechanical metamaterials such as open- and closed-cell lattice structures, foams, composites, and so...
Micromechanical modeling of material behavior has become an accepted approach to describe the macros...
Recent advances in machine learning have unlocked new potential for innovation in engineering scienc...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://...
The associated paper “Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Traini...
The paper “Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based...
Metamodels, or models of models, map defined model inputs to defined model outputs. Typically, metam...
More details about the data description can be found in the paper "Towards out of distribution gener...
The Mechanical MNIST Crack Path dataset contains Finite Element simulation results from phase-field ...
Figure 1: Examples produced by our data-driven finite element method. Left: A bar with heterogeneous...
La simulation des procédés thermomécaniques tels que le soudage demande une description fine des com...
This work involves the modeling and understanding of mechanical behavior of crystalline materials us...
Mechanical metamaterials such as open- and closed-cell lattice structures, foams, composites, and so...
Micromechanical modeling of material behavior has become an accepted approach to describe the macros...
Recent advances in machine learning have unlocked new potential for innovation in engineering scienc...