IACM Short Course

Relevance to WCCM–ECCOMAS

This short course will introduce practical and scientific machine learning methods for solid mechanics, with an emphasis on how modern data-driven methods can be formulated, interpreted, and deployed in mechanics problems.

Course description

This short course is intended for graduate students, researchers, and practitioners with a background in continuum mechanics, computational mechanics, materials, and applied physics. The course will introduce practical and scientific machine learning methods for solid mechanics, with an emphasis on how modern data-driven methods can be formulated, interpreted, and deployed in mechanics problems. It will also highlight how these methods can be used to integrate and automate AI-enabled workflows in both research and engineering practice.

The course will cover four main methodological directions: data-driven and neural network-augmented computing for nonlinear solid mechanics; thermodynamics-based data-driven methods for elastic and inelastic modeling; latent-space generative artificial intelligence for the inverse design of structures with target functionalities; and automatic engineering workflows with agentic AI.

The objective is to expose participants to recent advances across these four directions and to pair that overview with hands-on practical implementation. The course will therefore emphasize both the fundamentals of using advanced AI tools and their deployment in these specific mechanics and engineering applications. Case studies will demonstrate how these methods support data-driven computational solid mechanics, generative design of material systems and structures, and agentic AI workflows in which natural-language commands are used to automate data processing, modeling tasks, and parts of the engineering design process.

The course content is also informed by the organizers’ forthcoming book, Machine Learning for Solid Mechanics, currently in preparation for publication by Wiley.

Objectives and target groups

This short course is intended for graduate students, researchers, and practitioners with a background in continuum mechanics, computational mechanics, materials, and applied physics.

• Data-driven and neural network-augmented computing for nonlinear solid mechanics
• Thermodynamics-based data-driven methods for elastic and inelastic modeling
• Latent-space generative artificial intelligence for the inverse design of structures with target functionalities
• Automatic engineering workflows with agentic AI

Participants will be expected to bring their own laptops for the hands-on sessions, and course materials and sample code will be shared via a course website and an online repository before the event.

Scientific and technical areas covered

• Computational and methodological foundations for machine learning in solid mechanics
• Data-driven formulations and constitutive modeling
• Generative design of materials and structures
• AI-assisted engineering workflows
• Manifold-based representations of high-dimensional mechanical response data
• Neural operator frameworks for nonlinear, elastic, and inelastic solids
• Diffusion-based and latent space generative models
• Foundation models for engineering reasoning and workflow support
• Agentic AI systems in engineering computation