Special Session on
“Physics Informed Computational Intelligence”

Aim and Scope

Till five decades back, insights into almost all phenomena in Physics and Chemistry were obtained from experiments. Then techniques based on numerical simulations, where the governing equations in any domain were first expressed in their discrete forms and then solved using various classes of numerical algorithms, gradually acquired a dominant role. This speeded up acquisition of insights and understanding of phenomena which could be occasionally sensed through experiments. Importantly, the designs of alternative structures, materials and processes were also speeded up significantly. At this stage of the evolution of technology, where Machine Learning, Deep Learning and Optimization techniques (read Computational Intelligence) are literally pushing forward the frontiers of both Science and Applied Technologies in multiple domains, do these technologies contain in their womb the mechanisms to engender a second transformation?

Physics informed Neural networks are being gradually used for solutions of governing equations in domains like fluid mechanics, electromagnetics, structural mechanics, among others. However, there are multiple issues to be resolved, which makes this entire domain a rich field for research and numerical investigations. For example, resolution of turbulence phenomena and shocks in fluid mechanics remains a challenge. The most natural advantage of these techniques should operate in the field of optimization (of structures, materials and processes) where the Universal Approximation Theorem imbues these techniques with the ability to learn the solutions for a wide range of variables at one shot, from which the optimal one can be extracted with near-zero computational expense. But this works only when “glitches” in handling intrinsic field phenomena like those mentioned above, are resolved. Hence, this Special Session invites papers in the areas of:

  • Physics Informed Neural Networks (PINNs) in Fluid Mechanics
  • PINNs in Electromagnetics
  • PINNs in Structural Mechanics
  • PINNs in interdisciplinary phenomena
  • Applications to Engineering Design Optimization
  • Multiphysics Modelling
  • Transfer Learning in Physics Informed Models
  • Inverse problems for discovery of Hidden Physics
  • And other related areas.

Paper Submission

Potential authors may submit their manuscripts for presentation consideration through ICETCI 2023 submission system electronically at , following the conference guidelines. All submissions will go through peer review process. To submit your paper to this special session, you have to choose our special session title on the submission page.

Important Dates

Paper submission deadline: Mar 08, 2023
Mar 30, 2023
Apr 15, 2023
Paper acceptance notification date: June 05, 2023
June 08, 2023
Final paper submission and early registration deadline: June 25, 2023


Prof. Arya Kumar Bhattacharya, Dean Research, Professor and Head of CSE, Mahindra University, India
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