Next-Generation Physics-Informed Neural Network Approaches for Engineering Applications: Theory, Algorithms, and Simulations

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue of Algorithms focused on the next generation of modern neural network numerical techniques for solving problems in science and engineering, such as

• Physics-Informed Neural Networks (PINNs):
  • Conservative PINNs (cPINNs),
  • Variational PINNs (vPINNs),
  • Parareal PINNs (pPINNs),
  • Stochastic PINNs (sPINNs),
  • Fractional PINNs (fPINNs),
  • Bayesian PINNs (BPINN), 
  • Inverse PINNs (iPINN),
  • Extended PINNs (ePINN),
• Quantum Physics-Informed Neural Networks (QPINNs),
• Kolmogorov–Arnold-Informed Neural Networks (KINNs),
• Deep Operator Neural Network (DeepONet).

PINNs are deep learning models that combine the flexibility of neural networks and physical principles/laws, specifically ordinary or partial differential equations (PDEs), to solve complex engineering and scientific problems. The PINN numerical approach has gained popularity over the last two decades in solving problems involving PDEs, particularly in engineering applications, as well as in forward and inverse problems, and multiphysics.

Deep Operator Neural Network (DeepONet) is designed to learn and approximate operators to get a solution of problems in science and engineering. These operators can be operators of differentiation, integration, or PDE solutions and they map functions → functions.

This Special Issue is an exciting opportunity to present theories, algorithms, and simulations related to PINNs, QPINNs, KINNs, DeepONet and other next-generation physics-informed neural network approaches. We, alongside the journal Algorithms’ team, kindly invite you to submit a manuscript focused on any of the above research topics. Specific case studies with broad implications are also welcome. If you have any questions, please do not hesitate to contact us.

Dr. Aliki D. Mouratidou
Prof. Dr. Georgios Ε Stavroulakis
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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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 1800 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.