Massively parallel algorithms from physics and biology
DOI10.1080/00207160108805062zbMath0981.65161OpenAlexW2062486957WikidataQ113852483 ScholiaQ113852483MaRDI QIDQ2740954
Lishan Kang, Zhengjun Pan, Jun He, David J. Evans, Yuan-Xiang Li
Publication date: 9 September 2001
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160108805062
parameter estimationneural networksBoltzmann equationgenetic algorithmsevolution algorithmsartificial ecosystems
Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural networks for/in biological studies, artificial life and related topics (92B20) Rarefied gas flows, Boltzmann equation in fluid mechanics (76P05) Ecology (92D40) Parallel numerical computation (65Y05) Kinetic theory of gases in time-dependent statistical mechanics (82C40)
Uses Software
Cites Work
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- The parallel genetic algorithm as function optimizer
- Computational methods in optimization. A unified approach.
- A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws
- Numerical Navier-Stokes solutions from gas kinetic theory
- Fully-Lagrangian and lattice-Boltzmann methods for solving systems of conservation equations
- Lattice Boltzmann computational fluid dynamics in three dimensions
- Multilevel evolution: Replicators and the evolution of diversity
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- Lattice BGK Models for Navier-Stokes Equation
- CONVERGENCE OF GENETIC EVOLUTION ALGORITHMS FOR OPTIMIZATION
- Thermodynamics and Hydrodynamics for a Modeled Fluid
- A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems
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