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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


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 (






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



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



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



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.


[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.

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

[3] Dekhovich, A., Turan, O. T., Yi, J., & Bessa, M. A. (2022). Cooperative data-driven modeling. 1–13.