COMP20005 Engineering Computation Assessment Answer Australia

Writing Of COMP20005 Engineering Computation Assessment Answer Made Easy

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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List of Universities Offering Engineering Computation Course

• RMIT University
• Murdoch University
• University of Sydney
• University of Tasmania
• University of Queensland
• Monash University
• University of New Castle
• University of Melbourne
• Macquarie University
• Queensland University of Technology

The aforementioned universities in Australia are renowned for their quality of teaching and research and they offer distinct inter-related degree courses.

Structure of Course

The computational engineering course comprises the following units:

• Problem-solving using Algorithm
• Fundamental types of data: characters and numbers
• Errors and Approximation in numerical computation
• Structures of the fundamental program: selection, sequencing, functions, repetition
• Simple structures of data storage, arrays, structures, and variables
• Linear algebraic equations and roots of equations
• Splines and curve fitting
• Extrapolation and interpolation
• Numerical integration and differentiation

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Learning Outcomes

The learning outcomes of the computational engineering course are listed below:

• Develop basic numeric algorithms in MATLAB to compute/evaluate solutions of single linear equations, nonlinear equations, and for the management of basic linear algebra.
• Implementation of basic algorithms in case of curve fitting, interpolation, numerical integration, and numerical differentiation using MATLAB.
• Implementation of basic numerical methodologies to solve hyperbolic and parabolic partial differential equations and to process optimization using MATLAB.
• Demonstration of vital concepts of basic Bayesian inference, probability, and Monte Carlo technique for both integration as well as for evaluating basic probability problems.
• Formulation and solving basic problems involving statistics and probability in the basic applications of mechanical engineering.

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Career Opportunities

Computational engineering graduates can pursue careers in numerous sectors including aerospace, manufacturing, microelectronics, energy, health care, and many more:

• Computational Design or Developer
• Aerodynamics Engineer
• Bioinformatician
• Quantitative researcher
• Software Engineer
• Project Associate
• Computational Scientist
• Computational Biomechanics Researcher
• Computational Chemist
• Computational Fluid Dynamicist
• Computational Neuroscientist

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Discussion Forums

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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Peer-Reviewed Research Review Concerning the Use of Machine Learning in Computational Engineering

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.

Our professional experts are up-to-date in terms of knowledge, tools and techniques used in the related field. They suggest that new directions are required in research concerning computational engineering.

Our Experts Discuss the Approach to Derive Assignment Solution on COMP20005 Engineering Computation

Our highly qualified and experienced computational engineering experts have a firm grip on the subject. They encourage students to follow the below-mentioned approach to draft a perfect assessment solution:

• Check the type of assignment (tutorials, questions and answers, program formulations, essay, report, case studies, literature review, etc) and frame the solution accordingly.
• Note down the desired referencing style (APA, MLA, Chicago, etc).
• Look for the required formatting style before commencing the assignment solution.
• Carefully note down the overview of the assignment and then prepare a draft after conducting in-depth research by considering the latest and authentic sources.
• Write the assignment solution by strictly following the marking rubrics provided by the university.
• Incorporate their problem solving or critical thinking skills and knowledge of MATLAB, EXCEL, and Programming in completing all kinds of assessments.
• Collect, utilise and store data/information to support the decision-making process.
• Provide a comprehensive and efficient description of the desired concepts.
• Include all necessary elements and methodologies desired in the assignment.
• After completion of the assignment solution written in a perfect layout format with proper citation of references, read the file at least twice or thrice to rectify any error.

To have a clearer glimpse of the approach our online tutors follow to guide students with the solution, you can register on our website and download the COMP20005 Engineering Computation assignment sample online or an Engineeirng Assignment Sample. Few snapshots of the assessment tasks are depicted below for your reference:

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References

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.