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From mathematical models to supercomputers: How does computational science solve complex scientific problems?
With the rapid development of science and technology today, computing science has shown its unique charm and has become an important part of contemporary scientific research. This field not only maximizes the use of computing resources to advance our understanding in science and engineering, but also enables us to process complex data and models with higher accuracy and faster speed. Computational science is based on using the accuracy of mathematical models and computational simulations to predict, analyze, and solve a variety of problems.
Computational science is viewed as a third mode of science that complements and enhances experimental/observational and theoretical research methods.
So, what is computational science? It is an interdisciplinary field that combines mathematics, computing technology and scientific principles to solve practical and relevant problems through computer simulation and efficient numerical algorithms. Whether it’s managing huge amounts of biological data or simulating climate change, computational science plays a key role behind the scenes. Scientists and engineers in this field not only need to be proficient in mathematics and programming, but also need to fully understand the problems in their research fields.
When solving complex scientific problems, computational scientists need to go through a set of cyclical processes, including identifying problems, designing simulation frameworks, selecting computing infrastructure to maximize simulation performance, and verifying simulation results. This process tells us that no matter how technology develops, the understanding of real systems always requires continuous iteration and verification.
Every model and simulation in computational science is set within a specific domain, which means that any conclusions drawn are often closely related to the context.
Applications of Computational Science
Computational science has a wide range of applications. Among them, predictive computational science aims to predict specific aspects of physical events through mathematical models. These models start from initial and boundary conditions and incorporate parameters and uncertainties to help scientists sort out the probability of occurrence.
Another example is urban complex systems. In an era of increasing urbanization, it has become crucial to design and implement simulation models to predict urban development. Through such systems, it is possible to build a deeper understanding of urban dynamics and address the urban challenges that may arise in the future.
In the financial field, computational finance is facing unprecedented complexity. Many market participants and assets are interdependent. The measurement of danger and risk requires complex mathematical models to express. As the financial crisis intensifies, there is an even greater need to model market, credit and liquidity risks simultaneously to achieve a comprehensive understanding of risks.
In the development of biocomputing, new technologies are revolutionizing our understanding of biological and medical research.
In computational biology, advances in biotechnology allow us to process unprecedented amounts of complex data, such as real-time imaging of gene expression and cellular processes. Analysis of these data requires not only computational models, but also modeling to understand how genes control basic biological processes, such as biomineralization and embryonic development.
Prospects of Computing Science
With the advancement of technology, computing science continues to evolve and will become increasingly important in the future, whether it is solving complex scientific problems or improving the efficiency and accuracy of engineering design. However, this also raises many philosophical and methodological questions about the field, especially how the results of computational science are understood and verified. The validity of calculation results and the assumptions behind them are all mathematical models. Even the data generated by powerful supercomputers must be subject to rigorous scientific scrutiny.
As we develop towards more complex systems, how to establish reliable models and apply them to practical problems will become an important issue in promoting the development of computing science. Can we fully utilize the potential of computational science to obtain new insights and breakthroughs in future research?
