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Mathematics and computers are useful tools for modelling and drawing conclusions about the world around us. They are also useful tools for researching computers themselves. The fundamental ideas of sets, logic, combinatory and number theory are reviewed in this course to simulate real-world problems, understand how computers operate and write computer code. Learn how much a single Python line can accomplish. As students put together their toolkit of mathematical and programming techniques over the semester, the foundation for further study in mathematics, data science, or software development will be formed. Computational science is a popular choice of study in modern time and hence it is quite natural for diploma students to look for KIT103 assessment answers.

If you are pursuing a diploma in this unit, you can always connect with us and our experts will provide you with the best KIT103 Computational Science assignment help to help you easily complete the course.

KIT103 Assessment Answers

Learning Outcomes

Our KIT103 academic assistance professionals recommended these key learning goals since this unit is part of the sustainable engineering field of study. For the professional sector, they must be completed in order to get the desired results.

  1. Using finite mathematics methods to simulate real-world issues in a scientific computer setting.
  2. Create proper programme code from mathematical formulas involving discrete things.
  3. Choose the proper mathematics and coding structures for the given case.
  4. Without a computer's aid, abstract and practical issues are analysed based on the clear definition and characteristics of basic mathematical structures.

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What is Computational Science?

The focus of computational science is the design, use, and application of mathematical models to investigate and resolve scientific problems. The expression frequently refers to using computers to simulate or analyse scientific systems or quantitative processes.

Computational science is sometimes regarded as merging the three disciplines of science, computer science, and mathematics. The term "science" in this context refers to its theoretical and experimental components and is most often used to refer to a particular scientific area. Computation is a more recent scientific discovery than the other two well-established fields. Any behaviour, process, or system may be described and studied using theory, experimentation, and computing.

KIT103 assessment answers

Methodology of Computational Science

A mathematical model may adequately represent complex systems. Using these models, algorithms are created. These simulations and designs are performed in a computational environment to analyse and explain how a real system behaves.

Thus, to solve a problem through a computational scientific approach, the following steps are to be followed -

  • Theory - A model is built on the foundation of a scientific theory or the problem description.
  • Model - It is created a mathematical model of the system. It is possible to create algorithms that carry out analyses using the model. A simulation has predetermined rules that govern it.
  • Simulation/Analysis - Numerical analyses or simulations may be conducted based on algorithms. The method is utilised to utilise software and toolkits with the necessary capabilities.
  • Assessment - In some circumstances, the information from simulation findings and experimental outcomes is utilised to improve subsequent iterations. The procedure is repeated to solve the model or arrive at a conclusion.

Application of Computational Science

Any natural process, system, or phenomenon may be studied using computational science. It collaborates with several other scientific disciplines to develop solutions to scientific challenges.

Here are a few unique disciplines that computational science application has created -

  1. Computational Biology - Biology studies biological systems, including anatomical, ecological, evolutionary, behavioural, and social systems. Some distinct subcategories of computational biology are -
    • Computational Genomics - Through computational methods, gene sequencing has been made possible, and computational research has contributed significantly to our current understanding of genomes. Several examples of computational genomics applications include comparative genomics, gene expression analysis, gene evolution studies, mining biosynthetic gene clusters, and others.
    • Medical Computational Biology - Due to its ability to forecast human systems' behaviour, computational biology has many applications in medicine.
    • Computational Biomodeling - Computational biology allows for the modelling of biological systems, including the human body, at all scales, from the cellular to the organismal level. Modelling and utilising research is possible for everything from protein structures to brain function.
  2. Computational Chemistry - Computational chemistry is studying, predicting, and solving chemical problems using computers to understand molecules' structures and behaviours better. To build computational chemistry, chemists and quantum physicists work together. Most computational chemistry approaches are based on the Schrodinger equation. Different techniques are applied in computational chemistry. These various approaches have a variety of applications.
    • Molecular Modelling - To more quickly analyse x-ray diffraction data and comprehend molecular structures, computational modelling is used. This aids in predicting and interpreting molecular structures more accurately. Computational chemistry may also establish relationships between molecule structures and their physical characteristics.
    • Molecular Interactions - Pharmaceuticals is one of the most significant and well-known fields in which computational chemistry has been applied to understand molecular interactions. It is possible to create medications using computational modelling. Simulations are useful for predicting the outcomes of changing a drug's molecule. Drug design is expedited and made more effective, together with biomodelling of disease development.
  3. Computational Physics - Through computing and modelling, computational physics studies and analyses physical issues. Computational models are utilised in practically all areas of physics, and there are many applications in the field. Some of them include -
    • Computational Fluid Dynamics - In computational fluid dynamics (CFD), computational models are used to study fluid flow. Even though CFD employs a variety of strategies, the core technology is the same. The simulation is solved by iteration once the system is represented as a CAD model, a mesh is produced, and boundary conditions are established.
    • Computational Astrophysics - Theoretical techniques cannot be used to study or forecast large-scale astronomical occurrences with any degree of accuracy, and experimental techniques are not an option. Experimental techniques couldn't possibly investigate supernovas. By resolving computer models of supernovae, computational physics makes it possible to comprehend the birth and existence of stars.
    • Computational Electromagnetics - Here, electromagnetic interaction with physical elements in an environment is investigated using computer models.
  4. Computational Finance - Recently, computational finance has become quite popular and is now present in all facets of finance. The finest investments are frequently made with the assistance of experts in computational finance. Some finance jobs include -
    • Optimisation - Optimization issues involve trial and error via multiple distinct iterations, and computational science is well equipped to tackle them. Computing may be used to quickly and effectively complete tasks, including risk analysis, asset pricing, and portfolio optimisation.
    • Algorithm Trading - Making judgments in the trading industry requires careful consideration of several factors, including time, price, and volume. With the help of computational models and forecasting techniques, this procedure is automated through algorithms. An automated trading system based on algorithms is sometimes known as high-frequency trading.
  5. Computational Social Science - The goal of computational social science is to find solutions to social issues, such as human society's behavioural patterns, the development of culture, interpersonal interactions, economics, etc.
  6. Computational Sociology - Computational science allows for the modelling of social interactions at both local and big scales. It is possible to produce accurate behavioural prediction models for human civilisation using computational models.
  7. Computational Economics - This field focuses on forecasting economic models, unlike computational finance. With computer analysis, a society's economic tendencies and progress may be forecasted to some extent.

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KIT103 Assignment Sample Online

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KIT103 assessment answers1

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KIT103 Assessment Answers

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Frequently Asked Questions

A rapidly developing topic, computational science in engineering, uses computation's capacity to tackle difficult problems at the cutting edge of all engineering disciplines as well as social and natural science.

Researchers at academic universities, government laboratories, or tech firms frequently work as computational scientists. A computational scientist's duties include analysing massive data sets, frequently from astrophysics or related sciences since these frequently produce enormous data sets.

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