Documents published in Scipedia

• D5.1 (WP5): Definition of the demonstration actions

  C. García, E. Sanz, C. Soriano

  prodPhD OA (2022). 10

  
  

  Abstract

  The objective of this document is to describe the tasks carried out in task 5.1 of prodPhD project, which is devoted to design the demonstration actions to be carried out on the prodPhD Online Training Environment. The document first introduces briefly the content and organization of the training modules in the training environment. Then, it presents the supervising team, introduces the procedure for selection of candidates for the demonstration actions, the pre-demonstration training sessions, the technical support and feedback mechanism and the monitoring and postdemonstration feedback mechanisms. Finally, the development of the demonstration actions is described.

  Abstract
  The objective of this document is to describe the tasks carried out in task 5.1 of prodPhD project, which is devoted to design the demonstration actions to be carried out [...]

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• D4.1 (WP4): prodPhD microsite at Scipedia.com

  C. García, J. García-Espinosa

  prodPhD OA (2022). 9

  
  

  Abstract

  The objective of this document is to describe the tasks carried out in the frame of WP4 to develop a microsite in the platform Scipedia.com. This microsite enjoys the main capabilities of the open science platform Scipedia.com, and provide the framework to implement the prodPhD Online Training Environment, which will be customized to accomplish the project specific requirements.

  Abstract
  The objective of this document is to describe the tasks carried out in the frame of WP4 to develop a microsite in the platform Scipedia.com. This microsite enjoys the main [...]

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• D3.1 (WP3) Definition of the training methodology

  T. Bao, E. Sanz, C. García

  prodPhD OA (2022). 8

  
  

  Abstract

  This document presents the educational methodology aimed at providing PhD candidates with the necessary knowledge and skills to start and run their business, be they aspiring or confirming entrepreneurs. It addresses the way the training should be done, the training programme content in terms of ‘training pills’ and for each one, specify the rationale, the intended learning outcomes and the content, and suggest the activities to be undertaken, the duration and additional information sources

  Abstract
  This document presents the educational methodology aimed at providing PhD candidates with the necessary knowledge and skills to start and run their business, be they aspiring [...]

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• D7.3 (WP7): Events

  J. García-Espinosa

  prodPhD OA (2023). 6

  
  

  Abstract

  This document contains a list of the different events (conferences, seminars, webinars and workshops) which are organised by the project. This document will be updated and re-submitted at each reporting period.

  Abstract
  This document contains a list of the different events (conferences, seminars, webinars and workshops) which are organised by the project. This document will be updated and [...]

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• D2.2 (WP2): Needs and requirements analysis

  E. Sanz, M. Lascurain, A. Serrano, A. Haidar, P. Alonso

  prodPhD OA (2022). 2

  
  

  Abstract

   The prodPhD project aims to address the challenging problem of introducing entrepreneurship training in PhD programmes regardless of discipline. The prodPhD project will create the necessary teaching methodologies and the platform for applying them. The project consists of a consortium of four organizations from across Europe. The main objective of the prodPhD project is to implement innovative social network-based methodologies for teaching and learning entrepreneurship in PhD programmes. The multidisciplinary teaching and learning methodologies will enable entrepreneurship education to be introduced into any PhD programme, providing students with the knowledge, skills, and motivation to engage in entrepreneurial activities. The methodology will be conceived to develop experiential knowledge, involving academics, entrepreneurship experts, and mentors in its development and implementation. Besides, the exchange of experience, competences, and approaches facilitated by social networking will pave the way to crowdsourcing new ideas, improving training methodologies, and stimulating academics’ entrepreneurial skills

  Abstract
   The prodPhD project aims to address the challenging problem of introducing entrepreneurship training in PhD programmes regardless of discipline. The prodPhD project [...]

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• Minimally intrusive nonlinear Model Order Reduction

  R. Rossi, R. Bravo, J. Hernández, C. Roig

  Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021) Conference (2021). San Diego, USA (virtual conference). 26-29 September 2021

  
  

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• From Linear Mappings to Deep Learning for Model Reduction of Numerical Simulations of Industrial Interest:

  R. Bravo, R. Rossi, J. Hernández, C. Roig

  Computational Science and AI in Industry (CSAI 2021) Conference. (2021). Trondheim, Norway (virtual conference). 7-9 June 2021

  
  

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• A Local POD-HROM framework for fast and accurate numerical simulations

  R. Bravo, R. Rossi, J. Hernández

  XI International Conference on Coupled Problems in Science and Engineering (COUPLED 2021), (2021). Chia Laguna, Italy (virtual conference). 13-16 June,2021

  
  

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• Clustering Techniques for Enhanced Reduced Order Model Simulations in Structural Mechanics

  R. Bravo, R. Rossi, J. Hernández

  XVI International Conference on Computational Plasticity (COMPLAS 2021) (2021). Barcelona, Spain. 07-09 September 2021

  
  

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• D3.3 Report of ensemble based parallelism for turbulent flows and release of solvers

  R. Tosi, R. Codina, J. Principe, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2022). 6

  
  

  Abstract

  In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel with different initial conditions. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. Its error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time in order to minimize the initialization bias, accompanied by strategies to reduce simulation cost. The framework is validated on two very high Reynolds number obstacle problems of wind engineering interest in a high performance computing environment.

  Abstract
  In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. [...]

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