New Directions in the Application of Model Order Reduction
55:30
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1 year ago
Model order reduction (MOR) has played an important role in reducing computation time in important applications such as circuit simulation and structural analysis. Related methodology such as proper orthogonal decomposition and principal component analysis has seen numerous applications in areas such as computational fluid dynamics and protein dynamics. This methodology speeds computation and reduces storage requirements by replacing a large-scale system of differential or difference equations by one of substantially lower dimension that has nearly the same response characteristics.
New applications of MOR are beginning to emerge which have intriguing possibilities for the widespread use of this technology. Traditional applications have concerned dimension reduction of a single system. However, there are numerous important applications where there is a need to model a very large number of interactive systems all of the same type or to conduct parametric studies involving a large number of experiments such as a Monte Carlo study.
This talk will provide a brief review of existing MOR techniques and then will give an overview of current examples that suggest an exciting future for model reduction. Recent work has been done by several researchers in areas such as 1) probabilistic analysis of aerodynamic applications involving shape variations, 2) modeling of large systems of interacting neurons, 3) the dynamics of polymer solutions and melts. These systems share the common need to conduct very large numbers of computational experiments. Such experiments become intractable without the order of magnitude reductions in computation time achievable through model order reduction.Model order reduction (MOR) has played an important role in reducing computation time in important applications such as circuit simulation a...all »Model order reduction (MOR) has played an important role in reducing computation time in important applications such as circuit simulation and structural analysis. Related methodology such as proper orthogonal decomposition and principal component analysis has seen numerous applications in areas such as computational fluid dynamics and protein dynamics. This methodology speeds computation and reduces storage requirements by replacing a large-scale system of differential or difference equations by one of substantially lower dimension that has nearly the same response characteristics.
New applications of MOR are beginning to emerge which have intriguing possibilities for the widespread use of this technology. Traditional applications have concerned dimension reduction of a single system. However, there are numerous important applications where there is a need to model a very large number of interactive systems all of the same type or to conduct parametric studies involving a large number of experiments such as a Monte Carlo study.
This talk will provide a brief review of existing MOR techniques and then will give an overview of current examples that suggest an exciting future for model reduction. Recent work has been done by several researchers in areas such as 1) probabilistic analysis of aerodynamic applications involving shape variations, 2) modeling of large systems of interacting neurons, 3) the dynamics of polymer solutions and melts. These systems share the common need to conduct very large numbers of computational experiments. Such experiments become intractable without the order of magnitude reductions in computation time achievable through model order reduction.«
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