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Adaptive Multibody Methods Using a Divide and Conquer Framework (with Application to Biopolymers)


Kurt S. Anderson
Rensselaer Polytechnic Institute
Troy NY

October 19, 2018 at  10:00 AM
McConnell Engineering Room 437

Abstract:

Predicting important structural properties of large biomolecular systems such as RNA which play a critical role in various biological processes has gained increasing importance in recent years. In the modeling process of many of these systems, adaptive multiscale techniques may be desired to realize efficiency through an optimal combination of simulation speed and accuracy. For instance, consider the simulations of biomolecular systems with many thousands of atoms and associated degrees of freedom which involve temporal domains ranging from sub-femto seconds for covalently bonded atomic motions, to milliseconds for important conformational motions to emerge. The traditional approach for molecular modeling involves fully atomistic models which result in kinematically decoupled equations of motion. In spite of the simplicity of producing and solving the fully atomistic models, they become intractable for large systems due to the crippling cost of their associated forcing term calculation and the necessity of using exceedingly small integration step sizes.

Treating groups of atoms as rigid or flexible bodies connected via kinematic joints provides coarse grain structures to model a particular molecule. For larger systems, often a finest coarse grain structure is used for simulation. For most systems of interest, these coarse grained models are still prohibitively slow. Consequently, it is desired to generate and use the coarsest molecular system model which still provides results of acceptable accuracy. Due to the significant nonlinear coupling of the system equations of motion in the state variables and their derivatives, even modest changes in system state can render the existing coarsened model inadequate. Consequently, a coarse grain model which may perform well at one point of the simulation may perform poorly as the simulation progresses. Therefore, providing machinery to implement the simulation in an adaptive multiscale scheme is necessary. The goal is to provide the appropriate model resolution when and where it is needed. This leads to an articulated multibody representation of the system in which the degrees of freedom are added or taken away during the course of the simulation. Such adaptive simulations add several new computational challenges which are discussed in this talk.

Bio:

Dr. Anderson is a Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute. He spent many years in the aerospace industry working in the areas of dynamics, structural dynamics, and controls, as well as two years at the Institute for Applied Mechanics, at the Technical University of Darmstadt, Darmstadt Germany. His research interests are in all aspects of computational multibody dynamics, with particular attention given to development of advanced algorithms including but not limited to: Low computational order algorithms for dynamic systems simulation and control; Design sensitivity analysis of dynamic systems; Adaptive modeling; Parallel computing applications; Biopolymer dynamics; Unilateral constraints; And numerical integration schemes.

He is serving or has served as on the advisory or editorial boards of many top international journals associated with in dynamics, dynamics systems, and computational methods. He has organized multiple international conferences and is on the steering committee of many more.