Physics-Informed Learning: Applications in Physics-Informed Neural Networks and Machine Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 5
Special Issue Editors
Interests: computing for science & engineering; product & project management; operations research; software engineering; innovation management
Interests: inverse problems; heat and mass transfer; radiative transfer; computational intelligence; environmental modelling
Special Issue Information
Dear Colleagues,
The rapid evolution of Machine Learning (ML) and Deep Learning (DL) has provided powerful tools for analyzing complex data and building predictive models. However, traditional data-driven models often lack adherence to the fundamental laws of nature, leading to physically inconsistent or less generalizable results, particularly when data is sparse or noisy. This Special Issue focuses on Physics-Informed Learning (PIL), an emerging paradigm that seamlessly integrates domain-specific physical laws—expressed through partial differential equations (PDEs) or other constraints—directly into the ML model's architecture or loss function. We welcome contributions that explore novel theoretical foundations, methodological advancements, and practical applications of Physics-Informed Neural Networks (PINNs) and other physics-constrained ML techniques. Topics of interest include, but are not limited to, solving forward and inverse problems, uncertainty quantification, materials science, fluid dynamics, heat and mass transfer, and computational engineering, thereby pushing the boundaries of scientific computing and data-driven discovery by ensuring models are both data-consistent and physically plausible.
Prof. Dr. Rogerio De Carvalho
Prof. Dr. Antônio J. Silva Neto
Guest Editors
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Keywords
- physics-informed neural networks (PINNs)
- physics-informed machine learning (PIML)
- physics-informed learning (PIL)
- machine learning
- deep learning
- partial differential equations (PDEs)
- computational physics
- inverse problems
- computational engineering
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