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Advanced Reduced order modellinG: Online computational web server for complex parametric Systems

Periodic Reporting for period 1 - ARGOS (Advanced Reduced order modellinG: Online computational web server for complex parametric Systems)

Berichtszeitraum: 2022-07-01 bis 2023-12-31

The Proof of Concept “Advanced Reduced order modellinG: Online computational web server for complex parametric Systems” ARGOS wants to fulfil the demand of a Reduced Order Modelling Offline-Online tool, exploitable thanks to the creation of a computational interface, handling and analysing a parametric problem in a very intuitive way. ARGOS is a computational web server hosting several web applications, some with a general purpose and some for very specific problems: users can access directly through the web browser, using their available device, choose the application they need and finally use it to tackle the problem of interest. Thanks to the web application, users do not have to install any package by themselves on their machine. Any device having an internet connection can access ARGOS and run a web computing application. The real time response, due to the model reduction algorithms, combined with the ease of use of ARGOS, matches the industrial demand of intuitive and immediate tools. Within this black-box framework, the final user is not required to have any specific and technical knowledge about reduced order modelling. We thus envision that this approach could be adopted also in many other fields directly related with applied sciences, for example in medicine and environmental studies. ARGOS is going to have a vast audience of companies and institutions, proposing a closer integration between design teams and research and development teams. ARGOS could also be easily connected with emerging technologies related with data assimilation, data science and analytics, industrial digital twins, machine learning, diagnosis and maintenance tools, etc.
The mathematical core of the web platform ARGOS relies on the software developed within the ERC CoG AROMA-CFD project by valorising open source software libraries ITHACA and RBniCS, but also EzyRB, PyDMD, and PINA. Different demos have been developed, for example in bioengineering, structural mechanics, and industrial shape optimisation in fluid dynamics.
The main outcome is the establishment of FAST Computing as SISSA startup based on PoC ARGOS developments. The start-up is running and preparing project proposals as well as industrial projects.
There have been 3 different use cases developed to provide a wide audience with demonstrators:
1) argos.sissa.it as experimental computational webserver created with many worked problems/app in continuum mechanics (heat transfer, structural mechanics, fluid dynamics): 22 examples for a vast audience. This webserver demonstrates versatility and robustness of the platform and technology and it brings ARGOS closer to industry.
2) atlas.sissa.it is the experimental computational webserver created for biomedicine problems and to demonstrate the use of reduced order modelling for real time computing and visualisation, as well as real world data assimilation.
3) odyssea.sissa.it is the third computational webserver for digital twins (under development) to apply real time computing and analytics to predictive maintenance and performance optimisation. This webserver is in cooperation with Odyssea Live Demo project by SMACT competence center for Industry 4.0 in North-East of Italy.
Results beyond the state of the art include a more massive development in automatic learning algorithms to enhance computational performances for reduced order modelling with more important developments in data analytics, uncertainty quantification, as well as digital twins, as a new paradigm for computational sciences. These further tasks led to the creation of a new open source software library (PINA) based on physics informed neural networks to be integrated in our platforms.
A further strategic development deals with algorithms for predictive actions on complex systems/processes, like diagnosis and maintenance. This task has grown in importance and shown an important and significant potential.
Computational Pipeline
Technology Perspective for Scientific Computing