Computation 2020 Best Journal Paper Award (2020-2022)

  • Charissis, Vassilis (Recipient)

Prize: Prize (including medals and awards)


Computation is instituting the Best Paper Awards to recognize outstanding papers published in the journal. We are now pleased to announce the winners of the “Computation 2020 Best Paper Awards”.
Papers published in 2020 were preselected by the Computation Editorial Office on the basis of the number of citations and downloads from the website. The winners from the nominations were determined by the Editorial Board together with the Editorial Office.

Best Paper Award:
Machine-Learning Methods for Computational Science and Engineering
Michael Frank, Dimitris Drikakis and Vassilis Charissis.

Computation 2020, 8(1), 15; doi:10.3390/computation8010015
Available online:

Synopsis of the paper by the authors:
Over the last few decades, the re-kindled fascination with machine learning (ML) has spread into natural sciences and engineering. ML algorithms are increasingly developed for scientific computing-, physics-, and engineering-based processing. ML can assist in processing the terabytes of data produced by experiments and computations. However, extracting meaningful values for scientific and technological properties from such data is not always straightforward and can sometimes be just as time-consuming as the computations or experiments producing them. Traditionally, ML is often associated with signal and image processing, including self-driving vehicles, natural language processing, and optical character recognition. As many sectors invest significantly in ML to improve their products and services, ML algorithms have become ubiquitous in many scientific and technological areas. Our paper provided a comprehensive review of the state of the art in ML for computational science and engineering:
1. We discussed how ML could speed up or improve the quality of simulation techniques, such as computational fluid dynamics, molecular dynamics, and structural analysis.
2. We explored the ability of ML to produce computationally efficient surrogate models of physical processes-driven applications that circumvent the need for the more expensive simulation techniques entirely.
3. We showcased how ML can process large amounts of data, using examples from many diverse scientific fields, such as engineering, medicine, astronomy, and computing, and the emerging trend of using ML for more realistic and responsive Virtual Reality (VR) applications.
We believe that despite ML’s success and progress over recent years, it is still in its infancy, as many more computational challenges, such as accuracy and uncertainty, still need to be addressed.
Degree of recognitionInternational
Granting OrganisationsMDPI, St. Alban-Anlage 66


  • Machine Learning
  • Artificial Inteligence
  • Computational Fluid Dynamics
  • Virtual Reality
  • structural analysis
  • molecular dynamics
  • Digital Twins
  • Simulations
  • engineering
  • medicine
  • transportation