Machine Learning, particularly 'Deep Learning', has been a big player in the data-driven world of methods recently, thanks to the vast amount of available data stored and collected from billions of sensors and actuators. Since such data usually comes from multiscale physics problems and applications, data-driven approaches can produce physically inconsistent or implausible predictions while providing excellent accuracy.
In this scenario, a question arises: Can physics-informed machine learning be the new essential tool for solving challenging physics problems and applications?
Nowadays, a new frontier in Machine Learning is represented by combining physics laws and domain knowledge into the models (i.e. neural networks); in this way, such models can benefit from theoretical constraints on top of the observational data. The main goal is to improve the performance of the learning algorithms and introduce a new way of explaining and interpreting the obtained solutions.
The main research topics of this topical collection include, but are not limited to:
- Theory for Physics-Informed Neural Networks (PINNs)
- Neural Network architectures for PINNs
- Hybrid approaches for Physics-Informed Machine Learning
- Application of physical sciences to model and improve ML performance
- Scientific Machine Learning approaches for ODE and PDE
Lead Guest Editor:
Francesco Piccialli, University of Naples Federico II, Department of Mathematics and Applications “R. Caccioppoli”, Italy
David Camacho, Universidad Politecnica de Madrid, Spain
Gang Mei, University of Geosciences, Beijing, China
Initial Paper Submission: February 28, 2022
Initial Paper Decision: April 31, 2022
Revised Paper Submission: July 30, 2022
Final Paper Decision: August 30, 2022
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