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SC17-008: A Tutorial on the Framework for Data-Driven Design and Analysis of Structures and Materials (F3DASM)

Martin van der Schelling, Delft University of Technology
Miguel Bessa, Brown University
Jiaxiang Yi, Delft University of Technology
 

Summary:

The importance of Machine Learning in Computational Mechanics has dramatically grown in the last 5 years. Despite impressive progress, replicating our community's data-driven research results remains a challenge because we lack open-source and user-friendly frameworks. This short-course is focused on an overview of the data-driven process in Computational Mechanics and the corresponding open-source Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM) [1].

The framework integrates

  1. Design-of-experiments, where input features describe the microstructure, properties and external conditions of the system.
  2. Computational analyses, where a material response database is created.
  3. Machine learning, where we either train a surrogate model to fit our experimental findings.
  4. Optimization, where we iteratively improve the model to get an optimum design.

At the end of this short-course you will be able to replicate the results of a couple of research articles from the literature [2, 3] and, more importantly, be able to use different tools to perform new data-driven investigations to pursue your own research endeavor. Therefore, the learning objectives are:

  • Reviewing the data-driven process for computational mechanics;
  • Learning how to use F3DASM with the methods that are already implemented by following in-class tutorials;
  • Learning how to contribute with new methods for future projects of your interest; hence, contributing to the open-source project.

The F3DASM framework, the latest syllabus and content of the short-course is available on the F3DASM GitHub page (https://github.com/bessagroup/f3dasm).

Program:

Duration

Description

 

1h00

Part 1: Introduction to the F3DASM framework

  • Brief introduction to data-driven design for modeling of materials and structures
  • Introduction to the F3DASM framework: design-of-experiments, simulation, machine learning and optimization.

10 min

Break

1h30

Part 2: Practical session – fundamentals of the framework

  • Learn how to get familiar with the F3DASM using the design-of-experiments, machine learning and optimization sub-modules.
  • Hands-on exercises of establishing a machine learning model based on benchmark functions.

10 min

Break

1h30

Part 3: Setting up a computational experiment design

  • Learn to use the framework to perform new data-driven investigations to pursue your own research endeavor.
  • Illustrative example of a case study: super-compressible material design [2].
  • Learn how to contribute with new methods to the open-source project.

10 min

Break

1h30

Part 4: Practical session –  constitutive law prediction for composites

  • Case study: use the F3DASM framework to generate microstructures, do finite-element simulation and establish a machine learning model for constitutive law prediction [3].

Closing Remarks

Programming: Tutorials are in Python. Tutorials will be done with the help of Google Colab, therefore no installation is required other than having a Google account.

References:

[1] Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D. W., Brinson, C., Chen, W., & Liu, W. K. (2017). A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Computer Methods in Applied Mechanics and Engineering, 320(April), 633–667. https://doi.org/10.1016/j.cma.2017.03.037

[2] Bessa, M. A., Glowacki, P., & Houlder, M. (2019). Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible. Advanced Materials, 31(48), 1–6. https://doi.org/10.1002/adma.201904845

[3] Dekhovich, A., Turan, O. T., Yi, J., & Bessa, M. A. (2022). Cooperative data-driven modeling. 1–13. http://arxiv.org/abs/2211.12971