[ML-news] CFP: JIIP Special Issue on "Physics-Informed Machine Learning for problems in science"

[Evgeny Burnaev]

Journal of Inverse and Ill-posed Problems 

Special Issue on "Physics-Informed Machine Learning for problems in science"

Submission Deadline: October 1, 2025.

 

The special issue is dedicated to a quickly developing topic of solving inverse problems arising in science with the help of physics-informed machine learning. 

 

Inverse problems — where unknown causes are inferred from observed effects — are ubiquitous across scientific domains, from material science and geophysics to biomedical engineering and fluid dynamics. These problems are intrinsically ill-posed and often require additional domain knowledge that can act as a regularization of the model thus reducing uncertainty of the solution. Physics-informed machine learning proposed many ways to account for such a domain knowledge represented as equations or conservations laws or symmetries or in many other ways. Also, there is often important to estimate the results uncertainty to efficiently handle the risks.

 

This special issue highlights cutting-edge methodologies that address fundamental challenges in PIML for inverse problems while showcasing transformative applications across science and engineering. We invite contributions that advance theoretical foundations, computational frameworks, and real-world implementations.

 

The topic includes (but is not limited to) several types of inverse problems:

-      System parameters/equations identification;

-      Data assimilation;

-      History matching on measurements;

-      Building digital twins of complex systems;

-      Uncertainty estimation.

We also invite contributions that consider approaches to construction of physics-informed forward surrogate models, including physics-informed neural networks, neural operators, etc.

 

We hope that this issue will showcase the solutions in different scientific domains such as:

-      Geophysics (e.g., subsurface flow, seismics, climate, ocean and weather modelling)  

-      Fluid dynamics (e.g. turbulence field reconstruction from measurements)

-      Material property identification (e.g., core modelling, crack propagation)  

-      Biomedical imaging and source reconstruction

-      Other physics, chemistry, biology and medicine fields (e.g. electromagnetic and gravimetric inversion, protein modelling)

 

The submissions will be evaluated with a separate attention to the work reproducibility and availability of benchmarks and comparison to the traditional inverse techniques. Publishing open-source code and datasets is highly encouraged

 

Important Dates:

- Deadline for Manuscripts: 1^st of October 2025

- Expected Publication: 31^st of December 2025  

Article Types: original research, survey

 

Guest Editors

- Prof. Evgeny Burnaev (Skoltech, Russia)  

- Prof. N.M. Anoop Krishnan (Department of Civil Engineering, School of Artificial Intelligence (Joint Appt.), Indian Institute of Technology, Delhi Hauz Khas, New Delhi, INDIA)

- Prof. Ivan Oseledets (AIRI, Russia)

 

Submission Guidelines

1. Read the information for submitting an article at https://www.degruyterbrill.com/journal/key/jiip/html

2. Submit your manuscript at the journal webpage (https://mc.manuscriptcentral.com/jiip) and follow the submission procedure. 

Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript is submitted to this special issue. Send an email to the leading editor Prof. Evgeny Burnaev (E.Bu...@skoltech.ru) with subject “JIIP special issue PIML submission” to notify about your submission. Also, when submitting the manuscript, as an Editor please select “Burnaev, Evgeny” with a Reason “JIIP special issue PIML submission”.

3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.