189,201 research outputs found
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
Complex system simulation has been playing an irreplaceable role in
understanding, predicting, and controlling diverse complex systems. In the past
few decades, the multi-scale simulation technique has drawn increasing
attention for its remarkable ability to overcome the challenges of complex
system simulation with unknown mechanisms and expensive computational costs. In
this survey, we will systematically review the literature on multi-scale
simulation of complex systems from the perspective of knowledge and data.
Firstly, we will present background knowledge about simulating complex system
simulation and the scales in complex systems. Then, we divide the main
objectives of multi-scale modeling and simulation into five categories by
considering scenarios with clear scale and scenarios with unclear scale,
respectively. After summarizing the general methods for multi-scale simulation
based on the clues of knowledge and data, we introduce the adopted methods to
achieve different objectives. Finally, we introduce the applications of
multi-scale simulation in typical matter systems and social systems
Applications of MATLAB in Natural Sciences: A Comprehensive Review
In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines
Uncoupling System and Environment Simulation Cells for Fast-Scaling Modeling of Complex Continuum Embeddings
Continuum solvation models are becoming increasingly relevant in condensed
matter simulations, allowing to characterize materials interfaces in the
presence of wet electrified environments at a reduced computational cost with
respect to all atomistic simulations. However, some challenges with the
implementation of these models in plane-wave simulation packages still
persists, especially when the goal is to simulate complex and heterogeneous
environments. Among these challenges is the computational cost associated with
large heterogeneous environments, which in plane-wave simulations has a direct
effect on the basis-set size and, as a result, on the cost of the electronic
structure calculation. Moreover, the use of periodic simulation cells are not
well-suited for modeling systems embedded in semi-infinite media, which is
often the case in continuum solvation models. To address these challenges, we
present the implementation of a double-cell formalism, in which the simulation
cell used for the continuum environment is uncoupled from the one used for the
electronic-structure simulation of the quantum-mechanical system. This allows
for a larger simulation cell to be used for the environment, without
significantly increasing computational time. In this work, we show how the
double-cell formalism can be used as an effective PBC correction scheme for
non-periodic and partially periodic systems. The accuracy of the double-cell
formalism is tested using representative examples with different
dimensionalities, both in vacuum and in a continuum dielectric environment.
Fast convergence and good speedups are observed for all the simulation setups,
provided the quantum-mechanical simulation cell is chosen to completely fit the
electronic density of the system
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Computational Photochemistry, Spectroscopy, and Potential Energy Surfaces of Complex Molecules
University of Minnesota Ph.D. dissertation. June 2017. Major: Chemistry. Advisor: Donald Truhlar. 1 computer file (PDF); xvi, 248 pages.Computer simulation has become a useful tool for studying chemical reactions and spectroscopy. However, the reliable application of computational modeling to large and complex reactive systems involving electronically excited states is still limited. Two of the most important challenges in these applications are the accurate and efficient first-principles calculation of coupled ground- and excited-state potential energy surfaces (PESs), and the modeling of such PESs. For the former challenge, accurate electronic structure methods including static and dynamic electron correlation are often too costly for complex systems, while more affordable methods such as density functional theory (DFT) at their current stage of development are still not satisfactorily accurate for many such systems. For the latter challenge, the high dimensionality and complicated topography of the PESs of complex systems make it difficult to choose a model representation. This thesis presents several responses to these challenges: (a) Improvements to time-dependent DFT are made to provide better accuracy for excited states and for PESs. (b) A diabatization scheme is developed for more accurate and efficient modeling of coupled PESs. (c) Simple models are presented for efficient simulation of the band shape of the electronic spectroscopy of complex molecules. (d) State-of-the-art methods are applied to simulate the electronic spectrum, and to build the PESs for the photochemistry, of a complex reactive system, thioanisole
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
- …