About this Special Issue
Aerospace Engineering stands at the crossroads of precision and innovation, and the convergence of Physics-Informed Machine Learning (PIML) is reshaping how we approach modeling and design optimization. This Special Issue aims to showcase the latest research and developments in PIML for aerospace engineering, providing a platform for researchers and practitioners to share insights and innovations.
Scope
This Special Issue invites contributions related to the applications of Physics-Informed Machine Learning in aerospace engineering, including but not limited to the following areas:
1. Physics-Informed Machine Learning Models:
Development of novel PIML models that integrate domain-specific physics knowledge into machine learning algorithms for aerospace applications; and applications of PIML for modeling and predicting complex aerospace phenomena, such as aerodynamics, propulsion, and structural dynamics.
2. Design Optimization:
The use of PIML in design optimization of aircraft and spacecraft, including aerodynamic shape optimization, and structure design using novel materials or advanced manufacturing techniques.
3. Uncertainty Quantification:
Methods for quantifying and addressing uncertainty in PIML models for aerospace applications; and Bayesian approaches and probabilistic modeling techniques for handling uncertainty in PIML-driven aerospace system.
Prospective contributors are invited to submit original research articles, review articles, and case studies that address the topics outlined above. All submissions will undergo a rigorous peer-review process to ensure the quality, relevance, and originality of the published content.
Any questions? Please email the Editorial Office.
All article processing charges are being waived until the end of 2025 for submissions to Aerospace Research Communications.
Special Issue cover photo taken from the website:
https://www.einfochips.com/blog/how-artificial-intelligence-is-transforming-the-aerospace-industry/
Scope
This Special Issue invites contributions related to the applications of Physics-Informed Machine Learning in aerospace engineering, including but not limited to the following areas:
1. Physics-Informed Machine Learning Models:
Development of novel PIML models that integrate domain-specific physics knowledge into machine learning algorithms for aerospace applications; and applications of PIML for modeling and predicting complex aerospace phenomena, such as aerodynamics, propulsion, and structural dynamics.
2. Design Optimization:
The use of PIML in design optimization of aircraft and spacecraft, including aerodynamic shape optimization, and structure design using novel materials or advanced manufacturing techniques.
3. Uncertainty Quantification:
Methods for quantifying and addressing uncertainty in PIML models for aerospace applications; and Bayesian approaches and probabilistic modeling techniques for handling uncertainty in PIML-driven aerospace system.
Prospective contributors are invited to submit original research articles, review articles, and case studies that address the topics outlined above. All submissions will undergo a rigorous peer-review process to ensure the quality, relevance, and originality of the published content.
Any questions? Please email the Editorial Office.
All article processing charges are being waived until the end of 2025 for submissions to Aerospace Research Communications.
Special Issue cover photo taken from the website:
https://www.einfochips.com/blog/how-artificial-intelligence-is-transforming-the-aerospace-industry/
Keywords: physics-based machine learning, optimization, uncertainty quantification.
