A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust c...A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.展开更多
The report examines the evolution of computers from digital analogs through non-yon Neumann machines to quantum computers, which are also digital analogs. In the 60 years of digital analogs successfully developed at t...The report examines the evolution of computers from digital analogs through non-yon Neumann machines to quantum computers, which are also digital analogs. In the 60 years of digital analogs successfully developed at the Institute of Electromechanics of the USSR in Leningrad. An important stage in the development of non-classical multiprocessor machine performance and reliability has been the development of recursive machines, which was carried out at the Institute of Cybernetics led V.M.Glushkov and the Leningrad Institute of Aviation Instrumentation. The general approach to the synthesis is carried out through linguo- combinatorial modeling with structured uncertainty.展开更多
Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic...Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.展开更多
文摘A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.
文摘The report examines the evolution of computers from digital analogs through non-yon Neumann machines to quantum computers, which are also digital analogs. In the 60 years of digital analogs successfully developed at the Institute of Electromechanics of the USSR in Leningrad. An important stage in the development of non-classical multiprocessor machine performance and reliability has been the development of recursive machines, which was carried out at the Institute of Cybernetics led V.M.Glushkov and the Leningrad Institute of Aviation Instrumentation. The general approach to the synthesis is carried out through linguo- combinatorial modeling with structured uncertainty.
基金partly supported by National Natural Science Foundation of China (Grant No. 11272209)National Basic Research Program of China (Grant No. 2011CB309704)State Key Laboratory of Ocean Engineering of China (Grant No. GKZD010056).
文摘Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.
