In the Informatics for Design, Engineering And Learning (IDEAL) Lab, we study how to make machines that learn how to design and build other machines. To do this, we use Machine Learning, Artificial Intelligence, and Crowdsourcing to understand how large groups of people design things and how complex engineered systems work, so that we can use the data they produce to make them better.

Some of the fundamental scientific questions we study include: What are efficient and useful ways to computationally and mathematically represent designs? How do we combine physics-driven and data-driven models to design better products? What makes design collaboration between large groups of people work well or poorly? How can we use tools from applied mathematics (such as graph theory, category theory, and statistics) and computer science (such as complexity theory, submodular optimization, and artificial intelligence) to better understand how humans design?

Some past practical applications of our research include: a fully automated system for inferring what makes designs creative given human feedback; the world’s first polynomial time algorithm for diverse bi-partite b-matching; algorithms for exploring and optimizing high-dimensional design spaces (e.g., aircraft) that accelerate optimization by an order of magnitude or more; software for helping novices 3D print working mechanical devices; and network analyses of online collaborative design networks such as OpenIDEO.

Graduate and Post-Doctoral alumni from our lab have gone on to pursue productive research careers in Academia (MIT, American University), National Labs (NIH, NIST), and Industry (Siemens, Capital One, Amazon).

You can read more about our work by looking at our papers or by reproducing any of our open-source code.

We will be hosting the Frontiers in Design Representation Summer School 2022 at UMD from July 25-29, 2022 in College Park, MD. See the main Summer School website for more information and details on how to apply:

Posted 15 Jun 2022 by Mark

Mark recently had to give a pre-recorded invited talk as part of a conference. We uploaded the video to YouTube in case others wanted to watch as well. Video is 18 minutes long and covers much of the work covered in Wei Chen’s dissertation, including the following papers:

Posted 22 Jul 2020 by Mark

Our article entitled “Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks” was accepted for publication in the AIAA Journal. Check out the publications page for more information.

Posted 22 Jun 2020 by

Dr. Mark Fuge has received an NSF CAREER award to study new Machine Learning algorithms with the goal of enabling computers that learn how to design complex systems like new aircraft or vehicles. See the College of Engineering News Story for more details:

Posted 29 Jan 2020 by Mark
Published 20 Aug 2022
Published 14 Aug 2022
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Jun Wang, Wei (Wayne) Chen, Daicong Da, Mark Fuge and Rahul Rai CMAME 2022
Published 23 May 2022
Learning Airfoil Manifolds with Optimal Transport
Qiuyi Chen, Phillip Pope, Mark Fuge AIAA SciTech 2022
Published 10 Jan 2022
Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods
Qiuyi Chen, Jun Wang, Phillip Pope, Wei (Wayne) Chen and Mark Fuge JMD 2021
Published 06 Dec 2021
Published 17 Aug 2021
Automatically Discovering Mechanical Functions from Physical Behaviors Via Clustering
Kevin Chiu, David Anderson, and Mark Fuge ASME IDETC 2021
Published 21 May 2021
Published 03 Mar 2021
Posted 31 Dec 2023 by Mark
Posted 31 Dec 2022 by Mark
Posted 31 Dec 2021 by Mark
Posted 31 Dec 2020 by Mark
Posted 31 Dec 2019 by Mark
Posted 27 Dec 2019 by Mark
Posted 11 Nov 2019 by Mark
Posted 31 Dec 2018 by Mark
Posted 01 Sep 2015 by Mark
Posted 30 Aug 2014 by Mark