Integrating Machine Learning and Physics in Engineering and Biology
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 28 February 2026
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue delves into the transformative convergence of machine learning (ML) and physics, showcasing its profound impact across engineering and biological disciplines. It highlights how the data-driven capabilities of ML are being rigorously informed and enhanced by fundamental physical principles, leading to more robust, interpretable, and generalizable models.
A central theme is Physics-Informed Machine Learning (PIML), where physical laws, often expressed as partial differential equations, are embedded directly into ML architectures. This integration enables models to learn effectively from sparse data while adhering to known physical constraints, significantly improving predictive accuracy and reducing reliance on extensive datasets. Applications span diverse areas, including fluid dynamics, materials science, and climate modeling.
This Issue also explores the use of ML for the data-driven discovery of physical laws. Contributions demonstrate how techniques like symbolic regression and Gaussian processes can infer underlying physical relationships and constitutive equations from experimental data, accelerating scientific discovery in complex systems.
Furthermore, this collection addresses how ML can accelerate scientific simulations and design. By developing surrogate models or optimizing parameters, ML dramatically speeds up computationally intensive tasks such as molecular dynamics, finite element analysis, and quantum chemistry, facilitating the faster exploration of design spaces in both engineering and biological contexts.
In biology specifically, this integration is crucial for bridging scales, from molecular interactions to cellular and organismal behavior. Research presented includes ML applications in protein folding, drug discovery, biological transport, and biomechanics, consistently incorporating physical forces and energetic considerations.
Finally, a strong emphasis is placed on uncertainty quantification and interpretability. The included papers discuss methods for assessing the reliability of ML predictions and enhancing model transparency by grounding them in physical understanding, thus fostering trust and deeper scientific insights. This Special Issue serves as a vital resource for researchers aiming to leverage this powerful interdisciplinary synergy.
Dr. Yixiang Deng
Guest Editor
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.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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.
Keywords
- physics-informed machine learning
- AI for scientific discovery
- uncertainty quantification
- physical AI
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