Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design

TL;DR

The paper analyzes data publishing in mechanics and dynamics, arguing that data-centered design tasks in engineering demand publication practices that go beyond traditional first-principles reporting. It surveys the value, challenges, and guidelines for sharing data and code, with a focus on interpretability, benchmarking, and framing that engages data scientists in engineering design. Through seven concrete examples—ranging from vibrating plates to axial turbines—the work demonstrates practical ways to publish datasets, evaluation tools, and accompanying code, while deriving lessons on usability and validation. The work envisions a future where data-driven design assistants and AI-enabled workflows accelerate engineering progress, provided robust data publication practices and benchmarks are established.

Abstract

Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., in areas such as data-driven modeling, control and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems, e.g., in mechanics and dynamics, where design so far requires a lot of specialized knowledge. However, research-wise, compared to established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Possible ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice. The analysis, discussions, and examples are based on the research experience made in a priority program of the German research foundation focusing on research on artificially intelligent design assistants in mechanics and dynamics.

Figures (10)

• Figure 1: Example plates from the vibrating plates dataset along with the averaged mean squared velocity of the vibrations. Indentations are marked in white in the top row
• Figure 2: Crash box position in automobiles
• Figure 3: Pareto front for the objectives: maximum deformed length and total energy absorbed
• Figure 4: Images of a non-slender soft robot annotated with the reconstructed backbone (blue dashed line) and estimate points (white markers). Taken from Schindler2024b by CC BY
• Figure 5: DORA time series prediction task in the small-data limit: the training data (left) comprises two trajectories of period-2 cycle dynamics for two values of external forcing amplitudes. The modeling tasks aims at generalization to different forcing amplitudes that induce qualitatively different dynamics, among others chaotic dynamics, as displayed in the right