Computational engineering deals with the methods of building complex systems models. This new field of engineering involves the application and development of computational simulations and models in conjunction with high-performance computing. It mainly focuses on the establishment of causal models. Students pursuing computational engineering generally gain knowledge of engineering software, programming, and numerical methods and they apply such skills in solving engineering problems.
Students are most often allocated with heaps of assessments by their professors. They have to generally solve lots of numerical problems which require proficiency in Matlab programming and Excel spreadsheets. Students are generally not proficient enough as they don’t possess complete knowledge and get stuck while doing such assessments.
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The computational engineering course comprises the following units:
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The learning outcomes of the computational engineering course are listed below:
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Computational engineering graduates can pursue careers in numerous sectors including aerospace, manufacturing, microelectronics, energy, health care, and many more:
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There are several discussion forums like iMechanica, Engineers Edge, Eng-Tips, Engineering Clicks, COMSOL, etc are well-moderated and premier online destinations to get in touch with computational scientists, researchers, educators or engineers throughout the world. The community is highly knowledgeable and active and usually share their experiences, latest news, information, and also discuss the concepts of computational engineering and also questions about this field. They also conduct technical sessions and provide access to numerous tools, events and services for the understanding, development, and practice especially for subscribers throughout the world.
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Machine Learning (ML) algorithms are now commonly employed in computational engineering. ML helps to improvise pre-existing computational models (Frank et al., 2020). It has been reported that ML algorithms may replace terms of closure in the computational models and also replace computationally expensive and high-resolution techniques during multi-scale modelling.
The selection of the type of algorithm (SVM, GP, or ANN) relies upon training data availability and feature vector size. ML can also be utilised for surrogate modelling (Kochkov et al., 2021). It can replace it completely with a simplified and computationally favoured model. Approaches like SINDy, automated differentiation or physics-informed NNs also provide a robust framework for making predictions bounded by the laws of physics (Rani et al., 2021). Even, use of non-parametric algorithms like GPs has proven to be potentially very useful.
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Frank, M., Drikakis, D., & Charissis, V. (2020). Machine-learning methods for computational science and engineering. Computation, 8(1), 15.
Kochkov, D., Smith, J. A., Alieva, A., Wang, Q., Brenner, M. P., & Hoyer, S. (2021). Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21).
Rani, P., Kotwal, S., Manhas, J., Sharma, V., & Sharma, S. (2021). Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Archives of Computational Methods in Engineering, 1-37.
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