Computational Modeling Pdf

[Eliz Mettert]

Acomputational model uses computer programs to simulate and study complex systems[1] using an algorithmic or mechanistic approach and is widely used in a diverse range of fields spanning from physics,[2] engineering,[3] chemistry[4] and biology[5] to economics, psychology, cognitive science and computer science.[1]

The system under study is often a complex nonlinear system[6] for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments.[7] Operation theories of the model can be derived/deduced from these computational experiments.

Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, Computational Engineering Models (CEM),[8] and neural network models.

The CMDA program draws on expertise from three departments at Virginia Tech whose strengths are in quantitative science: Statistics, Mathematics, and Computer Science. By combining elements of these individual disciplines in innovative, integrated courses, with an emphasis on techniques at the forefront of applied computation, CMDA imparts a suite of quantitative skills that the workplace is demanding. The program focuses on extracting information from large data sets, as well as analyzing and solving problems by modeling, simulation, and optimization, drawing on the computational skills that make solving the complex problems of the 21st century possible. Graduates are expected to be qualified for positions in industry, business, the sciences, engineering, and more.

Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics and computer science. A computational model contains numerous variables that characterize the system being studied. Simulation is done by adjusting the variables alone or in combination and observing the outcomes. Computer modeling allows scientists to conduct thousands of simulated experiments by computer. The thousands of computer experiments identify the handful of laboratory experiments that are most likely to solve the problem being studied.

Weather forecasting models make predictions based on numerous atmospheric factors. Accurate weather predictions can protect life and property and help utility companies plan for power increases that occur with extreme climate shifts.

Flight simulators use complex equations that govern how aircraft fly and react to factors such as turbulence, air density, and precipitation. Simulators are used to train pilots, design aircraft, and study how aircraft are affected as conditions change.

Earthquake simulations aim to save lives, buildings, and infrastructure. Computational models predict how the composition, and motion of structures interact with the underlying surfaces to affect what happens during an earthquake.

Tracking infectious diseases. Computational models are being used to track infectious diseases in populations, identify the most effective interventions, and monitor and adjust interventions to reduce the spread of disease. Identifying and implementing interventions that curb the spread of disease are critical for saving lives and reducing stress on the healthcare system during infectious disease pandemics.

Clinical decision support. Computational models intelligently gather, filter, analyze and present health information to provide guidance to doctors for disease treatment based on detailed characteristics of each patient. The systems help to provide informed and consistent care of a patient as they transfer to appropriate hospital facilities and departments and receive various tests during their course of treatment.

Predicting drug side effects. Researchers use computational modeling to help design drugs that will be the safest for patients and least likely to have side effects. The approach can reduce the many years needed to develop a safe and effective medication.

Modeling infectious disease spread to identify effective interventions. Modeling infectious diseases accurately relies on numerous large sets of data. For example, evaluation of the efficacy of social distancing on the spread of flu-like illness must include information on friendships and interactions of individuals, as well as standard biometric and demographic data. NIBIB-funded researchers are developing new computational tools that can incorporate newly available data sets into models designed to identify the best courses of action and the most effective interventions during pandemic spread of infectious disease and other public health emergencies.

Tracking viral evolution during spread of infectious disease. RNA viruses such as HIV, hepatitis B, and coronavirus continually mutate to develop drug resistance, escape immune response, and establish new infections. Samples of sequenced pathogens from thousands of infected individuals can be used to identify millions of evolving viral variants. NIBIB-funded researchers are creating computational tools to incorporate this important data into infectious disease analysis by health care professionals. The new tools will be created in partnership with the CDC and made available online to researchers and health care workers. The project will enhance worldwide disease surveillance and treatment and enable development of more effective disease eradication strategies.

The mission of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) is to transform, through technology development, our understanding of disease and its prevention, detection, diagnosis, and treatment.

The MDIC Computational Modeling and Simulation Project was developed to achieve the delivery of medical product solutions in a responsible, patient sparing way. Through the use of computational modeling and simulation as valid scientific evidence, this effort balances the desire for certainty in the device performance while limiting the delay in patient access associated with increased certainty.

Dr. Veetil has extensive experience in designing and development of programs towards modernizing scientific workforce and practices by building academic-industrial-nonprofit collaboration through his work at National Institutes of Health (NIH) campus as the Lead Scientist and Program Manager for the Foundation for Advanced Education in the Sciences (FAES). Previously, Jithesh worked with Global Biological Standards Institute (GBSI), a Washington DC based non-profit as its Scientific Program Manager, leading the development and implementation of multiple programs on science policy, communications, and advocacy, including those related to cell line authentication, antibody validation, and reproducibility in biomedical research and development. Dr. Veetil also served as the Operations Manager for Preludesys Inc., working with international clientele from medical, IT, insurance and paralegal organizations on medical/healthcare data management.

Dr. Veetil completed his PhD in Biomedical Engineering at University of Arkansas, Fayetteville, AR, followed by postdoctoral fellowship at NIH. He has published numerous peer-reviewed manuscripts, reviews and book chapters. He also holds Masters in Biotechnology and Bachelors in Food Technology.

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This book consists of a series of papers focusing on the mathematical and computational modeling and analysis of some real-life phenomena in the natural and engineering sciences. The book emphasizes three main themes: (i) the design and analysis of robust and dynamically-consistent nonstandard finite-difference methods for discretizing continuous-time dynamical systems arising in the natural and engineering sciences, (ii) the mathematical study of nonlinear oscillations, and (iii) the design and analysis of models for the spread and control of emerging and re-emerging infectious diseases.

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