The Experts below are selected from a list of 45144 Experts worldwide ranked by ideXlab platform
Ilya Kuprov - One of the best experts on this subject based on the ideXlab platform.
-
polynomially scaling spin dynamics ii further state Space Compression using krylov subSpace techniques and zero track elimination
Journal of Magnetic Resonance, 2008Co-Authors: Ilya KuprovAbstract:We extend the recently proposed state-Space restriction (SSR) technique for quantum spin dynamics simulations [Kuprov et al., J. Magn. Reson. 189 (2007) 241–250] to include on-the-fly detection and elimination of unpopulated dimensions from the system density matrix. Further improvements in spin dynamics simulation speed, frequently by several orders of magnitude, are demonstrated. The proposed zero track elimination (ZTE) procedure is computationally inexpensive, reversible, numerically stable and easy to add to any existing simulation code. We demonstrate that it belongs to the same family of Krylov subSpace techniques as the well-known Lanczos basis pruning procedure. The combined SSR + ZTE algorithm is recommended for simulations of NMR, EPR and Spin Chemistry experiments on systems containing between 10 and 104 coupled spins.
-
polynomially scaling spin dynamics ii further state Space Compression using krylov subSpace techniques and zero track elimination
Journal of Magnetic Resonance, 2008Co-Authors: Ilya KuprovAbstract:We extend the recently proposed state-Space restriction (SSR) technique for quantum spin dynamics simulations [Kuprov et al., J. Magn. Reson. 189 (2007) 241–250] to include on-the-fly detection and elimination of unpopulated dimensions from the system density matrix. Further improvements in spin dynamics simulation speed, frequently by several orders of magnitude, are demonstrated. The proposed zero track elimination (ZTE) procedure is computationally inexpensive, reversible, numerically stable and easy to add to any existing simulation code. We demonstrate that it belongs to the same family of Krylov subSpace techniques as the well-known Lanczos basis pruning procedure. The combined SSR + ZTE algorithm is recommended for simulations of NMR, EPR and Spin Chemistry experiments on systems containing between 10 and 104 coupled spins.
Aldo Antognini - One of the best experts on this subject based on the ideXlab platform.
-
demonstration of muon beam transverse phase Space Compression
Physical Review Letters, 2020Co-Authors: Aldo Antognini, N J Ayres, Ivana Belosevic, V Bondar, Andreas Eggenberger, M Hildebrandt, Ryoto Iwai, D M Kaplan, K S KhawAbstract:We demonstrate efficient transverse Compression of a 12.5 MeV/c muon beam stopped in a helium gas target featuring a vertical density gradient and crossed electric and magnetic fields. The muon stop distribution extending vertically over 14 mm was reduced to a 0.25 mm size (rms) within 3.5 μs. The simulation including cross sections for low-energy μ^{+}-He elastic and charge exchange (μ^{+}↔ muonium) collisions describes the measurements well. By combining the transverse Compression stage with a previously demonstrated longitudinal Compression stage, we can improve the phase Space density of a μ^{+} beam by a factor of 10^{10} with 10^{-3} efficiency.
K S Khaw - One of the best experts on this subject based on the ideXlab platform.
-
demonstration of muon beam transverse phase Space Compression
Physical Review Letters, 2020Co-Authors: Aldo Antognini, N J Ayres, Ivana Belosevic, V Bondar, Andreas Eggenberger, M Hildebrandt, Ryoto Iwai, D M Kaplan, K S KhawAbstract:We demonstrate efficient transverse Compression of a 12.5 MeV/c muon beam stopped in a helium gas target featuring a vertical density gradient and crossed electric and magnetic fields. The muon stop distribution extending vertically over 14 mm was reduced to a 0.25 mm size (rms) within 3.5 μs. The simulation including cross sections for low-energy μ^{+}-He elastic and charge exchange (μ^{+}↔ muonium) collisions describes the measurements well. By combining the transverse Compression stage with a previously demonstrated longitudinal Compression stage, we can improve the phase Space density of a μ^{+} beam by a factor of 10^{10} with 10^{-3} efficiency.
Debra J. Searles - One of the best experts on this subject based on the ideXlab platform.
-
new observations regarding deterministic time reversible thermostats and gauss s principle of least constraint
Journal of Chemical Physics, 2005Co-Authors: Joanne N Bright, Denis J. Evans, Debra J. SearlesAbstract:Deterministic thermostats are frequently employed in nonequilibrium molecular dynamics simulations in order to remove the heat produced irreversibly over the course of such simulations. The simplest thermostat is the Gaussian thermostat, which satisfies Gauss’s principle of least constraint and fixes the peculiar kinetic energy. There are of course infinitely many ways to thermostat systems, e.g., by fixing ∑i∣pi∣μ+1. In the present paper we provide, for the first time, convincing arguments as to why the conventional Gaussian isokinetic thermostat (μ=1) is unique in this class. We show that this thermostat minimizes the phase Space Compression and is the only thermostat for which the conjugate pairing rule holds. Moreover, it is shown that for finite sized systems in the absence of an applied dissipative field, all other thermostats (μ≠1) perform work on the system in the same manner as a dissipative field while simultaneously removing the dissipative heat so generated. All other thermostats (μ≠1) are thu...
-
new observations regarding deterministic time reversible thermostats and gauss s principle of least constraint
arXiv: Statistical Mechanics, 2005Co-Authors: Joanne N Bright, Denis J. Evans, Debra J. SearlesAbstract:Deterministic thermostats are frequently employed in non-equilibrium molecular dynamics simulations in order to remove the heat produced irreversibly over the course of such simulations. The simplest thermostat is the Gaussian thermostat, which satisfies Gauss's principle of least constraint and fixes the peculiar kinetic energy. There are of course infinitely many ways to thermostat systems, e.g. by fixing $\sum\limits_i{|{p_i}|^{\mu + 1}}$. In the present paper we provide, for the first time, convincing arguments as to why the conventional Gaussian isokinetic thermostat ($\mu=1$) is unique in this class. We show that this thermostat minimizes the phase Space Compression and is the only thermostat for which the conjugate pairing rule (CPR) holds. Moreover it is shown that for finite sized systems in the absence of an applied dissipative field, all other thermostats ($\mu=1$) perform work on the system in the same manner as a dissipative field while simultaneously removing the dissipative heat so generated. All other thermostats ($\mu=1$) are thus auto-dissipative. Among all $\mu$-thermostats, only the $\mu=1$ Gaussian thermostat permits an equilibrium state.
Paul Francois - One of the best experts on this subject based on the ideXlab platform.
-
numerical parameter Space Compression and its application to biophysical models
Biophysical Journal, 2020Co-Authors: Chiehting Jimmy Hsu, Gary J Brouhard, Paul FrancoisAbstract:Abstract Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter Space Compression (PSC), in which a subset of parameters emerges as “stiff” as a function of time or Space. PSC has only been used to explain analytically solvable physics models. We have generalized this result by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically solvable models of a random walk with drift and protein production and degradation. We then applied our method to a simple computational model of microtubule dynamic instability. We propose that numerical PSC has the potential to identify the low-dimensional structure of many computational models in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.
-
numerical parameter Space Compression and its application to microtubule dynamic instability
arXiv: Biological Physics, 2018Co-Authors: Gary J Brouhard, Paul FrancoisAbstract:Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e. are sensitive to) only a subset of those parameters. Rigorously identifying this subset of "stiff" parameters has been made possible by the development of parameter Space Compression (PSC). However, PSC has only been applied to analytically-solvable physical models. We have generalized this powerful method by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically-solvable models of random walk with drift and protein production and degradation. We then applied our method to an active area of biophysics research, namely to a simple computational model of microtubule dynamic instability. Such models have become increasingly complex, perhaps unnecessarily. By adding two new parameters that account for prominent structural features of microtubules, we identify one that can be "compressed away" (the "seam" in the microtubule) and another that is essential to model performance (the "tapering" of microtubule ends). Furthermore, we show that the microtubule model has an underlying, low-dimensional structure that explains the vast majority of our experimental data. We argue that numerical PSC can identify the low-dimensional structure of any computational model in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.
