Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI

Dear Colleagues,

In recent years, the fusion of bio-inspired methodologies with data-driven techniques has created a transformative wave that is sweeping across various engineering disciplines. This unique combination has ushered in a new era of innovation, especially in fields such as control systems, optimization techniques, and artificial intelligence (AI) applications. Traditional engineering practices, which once relied heavily on theoretical models and empirical analyses, have now begun to harness the power of data-driven approaches, complemented by insights drawn from biological systems, to enhance their efficiency, accuracy, and adaptability.

This Special Issue aims to examine the forefront of this paradigm shift, focusing on bio-inspired, data-driven methodologies within the realm of engineering. Specifically targeting control, optimization, and AI, this Special Issue seeks to unravel the complex interplay between data-driven techniques and insights from the natural world. The profound impact of integrating bio-inspired concepts with data-driven methodologies in engineering cannot be overstated. Through the lens of control systems, these approaches offer dynamic, adaptive solutions that mimic biological systems, enabling the precise and responsive management of complex systems. Optimization techniques, bolstered by data-driven insights and bio-inspired algorithms, push the boundaries of traditional methods, unlocking new avenues for efficiency and performance enhancement across various engineering fields. Meanwhile, AI applications, inspired by the cognitive and adaptive capabilities of biological organisms, are set to revolutionize engineering practices with their intelligent, self-optimizing solutions.

However, this promising convergence of bio-inspired and data-driven methodologies with engineering practices presents significant challenges. Achieving harmonious integration requires a deep understanding of biological principles, data science, and engineering disciplines, as well as the ability to contend with issues of interpretability, reliability, and scalability. Moreover, this fusion requires robust frameworks for data collection, preprocessing, and analysis, ensuring that insights drawn from vast datasets are both relevant and aligned with bio-inspired principles.

This Special Issue invites contributions that explore the cutting-edge bio-inspired, data-driven methodologies emerging in engineering. Through collaborative research, practical applications, and theoretical discussions, we aim to highlight the transformative potential of these methodologies. By fostering dialogue, sharing insights, and promoting further advancements, this Special Issue seeks to chart a course towards a future where engineering practices are empowered by the synergy between bio-inspired insights and data-driven approaches, achieving unprecedented levels of efficacy, adaptability, and innovation.

We welcome original research contributions addressing, but not limited to, the following topics:

1. The design and implementation of bio-inspired, data-driven control systems;
2. Machine learning and deep learning techniques for the optimization of engineering, inspired by natural processes;
3. Reinforcement learning approaches to control and optimization problems, with insights drawn from biological systems;
4. The use of bio-inspired methodologies for data-driven modelling and predictive maintenance in engineering systems;
5. The integration of bio-inspired methods with traditional control and optimization techniques;
6. Applications of bio-inspired, data-driven approaches in robotics, automation, and autonomous systems;
7. Case studies and real-world applications showcasing the effectiveness of bio-inspired, data-driven methods in different engineering domains;
8. The interpretability, robustness, and reliability of bio-inspired, data-driven control, optimization, and AI techniques.

Dr. Ameer Tamoor Khan
Prof. Dr. Shuai Li
Dr. Bolin Liao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• bio-inspired
• biomimetics
• optimization
• data-driven
• machine learning
• LLM