Computational science is a multidisciplinary field that uses practical mathematical algorithms with computer science to study issues of other sciences and engineering. The scientists of computational science will combine the expertise of one or more areas in order to model and simulate real phenomena on the computers, and analyze the data from the massive data sources.
Computational science is considered as the third pillar (along with theoretical science and experimental science) in discovering knowledge and contributing significantly to the scientific and technological revolution. So far, there is an indispensable part of computational science in every aspect of life these days.
Computational science will create “virtual labs on the computer” to model and simulate most phenomena and things in life. This will save huge amounts of money and time for development in many different areas. Modeling and simulation in computational science have been used in almost all scientific and technical disciplines such as meteorology, aeronautics, mechanics, materials, astronomy, particle physics, environment, biomedicine, etc.
Computational science can be divided into many sub-disciplines such as computational mechanics, computational physics, computational mathematics, computational chemistry, computational biology, computational materials, computational statistics, etc. All these minor disciplines share the same up-to-date computational techniques, so computational science can be more widely understood as Computational Science and Engineering.
The role of computational science in the 4.0 industrial revolution
- Computational science and data science are the key technologies of the present and future, as well as of the 4th industrial revolution. Computational methods and mathematical analysis used for simulation, prediction, data evaluation, analysis, risk assessment, development, and optimization can be found almost everywhere.
- Modeling, mathematics, statistics, data, and algorithms are the foundation of the digital world, which assist in connecting and blurring the boundary between the real world and the virtual world. By combining mathematical and statistical knowledge with computer science, computational science and data science will provide breakthrough solutions for our research, economics, and society. By taking advantage effectively the power of modern computational mathematics and supercomputers, we can solve increasingly complex problems in economics, finance, medicine, R&D, natural science, environmental and social issues, biomedicine and etc.
The role of computational science in other sciences
- Despite being a separate discipline, Computational science plays a paramount role in the development of other sciences and techniques. This is highly reflected in the 2005 Report titled “Computational Science: Ensuring America’s Competitiveness” to the US President by the President’s Information Technology Advisory Committee”. “The computational science is now an indispensable solution to solve complex problems in every field. The research achievements which have promising great economic and scientific value will be stemmed from those who are capable of using advanced computational technologies, as well as the applications of Computational science”, said the report.
Some applications of Computational science to other disciplines may be listed as:
Meteorologists use computer simulations to predict weather, long-term changes in climate, and study phenomena such as cyclones and tornadoes. This task requires the ability to handle up to hundreds of terabytes of data, along with a lot of parallel microprocessor systems that can accurately simulate the phenomenon occurring on an area of several hectares;
Aeronautical engineers use computers to simulate the airflow around the aircraft, thereby identifying aircraft attributes and structures in the harshest conditions: saving warehouses, materials and being able to repeat the experiment on computers;
Mechanical engineers simulate car accidents to design safety features: saving space, materials and being able to repeat experiments on computers;
In biomedical science, the functioning of organs in the human body is perfectly simulated at the cellular level, which is the basis for experiments on infection and disease progression, in addition to responding of the body with a vaccine or new remedies;
Epidemiology has developed computational model-based laboratories to simulate the spread of infectious diseases through a population. These programs may provide scenarios to assist planners to visualize the results of strategies such as vaccination and quarantine in the face of a pandemic. The emergence of the latest Lyme disease, HIV/AIDS, SARS, and avian influenza has increased the stature and visibility of epidemiological models as a crucial public health tool;
In the production and operation of enterprises, specialized software running on the computer network is used to manage the complex information streams from materials, finance, etc. Subsequently, it is synthesized to make a supply chain managed on the computer. This information system help increase efficiency, reduce operating costs as well as bring more competitive advantages for the enterprises;
Computer-based economic models have been becoming essential tools for quickly and accurately analyzing not only microdata but also macroeconomic ones, which allows for early forecasting of market conditions.
Different models of computer simulations and traffic scenarios have been becoming vital tools for providing traffic control scenarios to minimize traffic congestion in major cities.
ARTIFICIAL INTELLIGENCE (AI)
AI is a branch of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. AI systems can be designed to operate in a range of environments, from small devices like smartphones and home appliances to large systems like self-driving cars and intelligent robots.
There are different types of AI, including rule-based systems, machine learning, and deep learning. Rule-based systems use a set of predefined rules to make decisions, while machine learning involves training a model on large amounts of data to make predictions. Deep learning is a subset of machine learning that involves training neural networks with multiple layers to learn more complex features.
AI has a range of applications in areas such as healthcare, finance, transportation, and customer service. Some examples of AI technologies in use today include virtual assistants like Siri and Alexa, image and speech recognition systems, and self-driving cars. As AI technology continues to evolve, it has the potential to revolutionize many industries and transform the way we live and work.
Despite the potential benefits of AI, there are also concerns about the impact it may have on society. Some worry that AI systems could lead to job displacement, as machines take over tasks traditionally performed by humans. There are also concerns about the potential misuse of AI, such as the use of facial recognition technology for surveillance.
To address these concerns, there are ongoing discussions about how best to regulate and govern AI. Some organizations have proposed ethical guidelines and principles for AI development, which include values such as transparency, accountability, and human-centered design.
As AI technology continues to develop, there are also many exciting research areas that are being explored. For example, researchers are working on developing AI systems that can learn from smaller amounts of data, as well as systems that can explain their decision-making processes to humans.
In summary, Artificial Intelligence is a rapidly evolving field with the potential to revolutionize many aspects of our lives. While there are concerns about the impact of AI on society, ongoing research and discussions around ethical guidelines and regulation are helping to ensure that AI is developed in a responsible and beneficial way.