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The monograph deals with the state of the art of current advances and developments in reduced order methods for modeling and computational reduction of complex parametrized systems, held by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques. All the contributions in the book have been peer-reviewed and contain examples of applications in computational mechanics.
Model reduction topics are emphasized in several themes: 1. design, optimization, and control theory in real-time with applications in engineering; 2. data assimilation, geometry registration, and parameter estimation with a special attention to real-time computing in biomedical engineering and computational physics; 3. real-time visualization of physics-based simulations in computer science; 4. the treatment of high-dimensional problems in state space, physical space, or parameter space; 5. the interactions between different model reduction and dimensionality reduction approaches; 6. the development of general error estimation frameworks which take into account both model and discretization effects.
This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics.
Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account bothmodel and discretization effects.
This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.
Model reduction topics are emphasized in several themes: 1. design, optimization, and control theory in real-time with applications in engineering; 2. data assimilation, geometry registration, and parameter estimation with a special attention to real-time computing in biomedical engineering and computational physics; 3. real-time visualization of physics-based simulations in computer science; 4. the treatment of high-dimensional problems in state space, physical space, or parameter space; 5. the interactions between different model reduction and dimensionality reduction approaches; 6. the development of general error estimation frameworks which take into account both model and discretization effects.
This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics.
Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account bothmodel and discretization effects.
This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.