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Kinematics is a subfield of physics and mathematics, developed in classical mechanics, that describes the motion of points, bodies (objects), and systems of bodies (groups of objects) without considering the forces that cause them to move.
Comprehensive life science modeling and simulation suite of applications focused on optimizing drug discovery process: small molecule simulations, QM-MM, pharmacophore modeling, QSAR, protein-ligand docking, protein homology modeling, sequence analysis, protein-protein docking, antibody modeling, etc. Proprietary, trial available
In a minimal model for an ion channel, there are two states: open and closed. However, other states are often needed in order to accurately represent the data, including multiple closed states as well as inactive and/or desensitized states, which are non-conducting states that can occur even in the presence of stimulus. [9]
Differs from traditional system dynamics approaches in that 1) it puts much greater emphasis on probabilistic simulation techniques to support representation of uncertain and/or stochastic systems; and 2) it provides a wide variety of specialized model objects (beyond stocks, flows and converters) in order to make models less abstract (and ...
These problems can also be modelled by the molecular dynamics method. The difference is that this approach relies on equilibrium statistical mechanics rather than molecular dynamics. Instead of trying to reproduce the dynamics of a system, it generates states according to appropriate Boltzmann distribution.
The first applications of computer simulations for dynamic systems was in the aerospace industry. [5] Commercial uses of dynamic simulation are many and range from nuclear power, steam turbines, 6 degrees of freedom vehicle modeling, electric motors, econometric models, biological systems, robot arms, mass-spring-damper systems, hydraulic systems, and drug dose migration through the human body ...
A direct numerical simulation (DNS) [1] [2] is a simulation in computational fluid dynamics (CFD) in which the Navier–Stokes equations are numerically solved without any turbulence model. This means that the whole range of spatial and temporal scales of the turbulence must be resolved.
Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. [1] Given a series of snapshots of a dynamical system and its corresponding time derivatives, SINDy performs a sparsity-promoting regression (such as LASSO) on a library of nonlinear candidate functions of the snapshots against the derivatives to find the governing equations.