DESTION 2026 will take place on May 11th, 2026 in Saint Malo, France as part of the CPS-IoT Week events.

The target audience of DESTION 2026 is researchers and practitioners of Cyber Physical Systems (CPS) design methodologies, machine learning, experts from the tool industry, and end-users from systems companies engaged in CPS and Internet of Things (IoT) development. Over the last few years, there has been transformative progress in AI/ML methods such as learning accurate surrogate models, generative AI, efficient design space exploration, testing and verification, and formal synthesis. This progress coupled with the rapidly growing scale and complexity of CPS and IoT has fueled immense interest in the development of design automation tools. The primary emphasis of the Workshop is on discussing and demonstrating new design tool concepts, methodologies, implementations, and case-studies for design, verification and testing of CPS and IoT.

Overview

Cyber-Physical Systems such as aircraft, automobiles, industrial robots, medical devices, and Internet-of-Things applications promise significant economic and societal benefits. However, the design, verification, validation, testing, and operation of such systems present several challenges induced by scale, complexity, uncertainty, and many stringent requirements on safety, performance, security, availability, and many other metrics. Over the past decades, product development has shifted from largely mechanical systems with loosely coupled components to highly integrated cyber-physical architectures with dense software-hardware interactions. This transition has dramatically expanded design complexity, often resulting in multi-year development cycles with numerous redesign loops. Current industrial practice frequently relies on "hot-starting" new products from existing baselines - an approach that reduces risk but inhibits innovation by limiting systematic exploration of the vast design space.

Recent advances in AI for Design Automation offer a promising path forward. Physics-informed and neuro-symbolic machine learning can accelerate simulation; probabilistic modeling and Bayesian methods provide quantification of uncertainty; abstraction reduces design search complexity; generative techniques find shortcuts through complex design spaces. At the same time, the rise of AI-enabled CPS creates the need for Design Automation for AI, including neural architecture search, model verification, runtime testing, ontology-driven modeling, heterogeneous simulation, neuro-symbolic learning, and the use of foundation models in engineering workflows.

Format

This full-day workshop will provide a premier forum for researchers and engineers from academia, industry, and government to present and discuss challenges, promising solutions, and applications in design automation for CPS and IoT. DESTION 2026 will have a broad scope covering techniques and tools for modeling, simulation, synthesis, validation, and verification of CPS and IoT, with a focus on “AI for Design Automation” and “Design Automation for AI”, and their applications in a variety of domains, such as automotive and transportation systems, avionics, robotics, building architectures, grid, and medical devices.

The workshop will feature a combination of invited talks (45 minutes) and contributed talks on accepted papers (20 minutes). The program will be organized into 3–4 thematic sections, each centered on a specific topic and anchored by an invited speaker. The workshop will conclude with a 15–30 minute discussion and wrap-up session. 

About DESTION

DESTION provides a premier forum for researchers and engineers from academia, industry, and government to present and discuss challenges, promising solutions, and applications in design automation for CPS and IoT. DESTION 2026 has a broad scope covering techniques and tools for modeling, simulation, synthesis, validation, and verification of CPS and IoT, with a focus on "AI for Design Automation" and "Design Automation for AI", and their applications in a variety of domains, such as automotive and transportation systems, avionics, robotics, building architectures, grid, and medical devices.

CALL FOR PAPERS

Coming soon!