Modern engineering is driven by computational workflows for modeling, simulation, and design. Machine learning methods show promise for augmenting engineering modeling, simulation and design across many application areas. Success stories from recent research include predicting the behavior of materials, using ML for physical simulation and design optimization, creating generative models of 3d objects and designs, optimizing energy systems to improve performance and reduce environmental impact, and many more.
This workshop aims to bring together the machine learning and engineering communities to foster collaboration and spur new research in the emerging field at their intersection. Broadly speaking, we are interested in work that develops or investigates machine learning methdos for application to engineering problems. Some ideas of specific topics we want to see addressed include, but are not limited to:
- Learning surrogate and reduced-order models for physical simulators,
- Using machine learning to accelerate design optimization, system identification and control,
- Developing ML tools which can assist engineers to specify, explore and evaluate possible systems and designs,
- Using ML to accelerate prototyping and manufacturing,
- Integrating ML tools with preexisting engineering tools and workflows,
- Development of software and libraries to enable all of the above,
- Identification of applications and pressing challenges/opportunities in the application of ML to engineering.