Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…In contrast, for smaller and denser systems such as diamond nanoparticles, especially if employing large basis sets, algorithms that use the underlying molecular orbital coefficients offer superior performance, despite their higher formal scaling. …”

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…Functional groups are widely used in organic chemistry, because they provide a rationale to analyze physicochemical and reactivity properties. …”

CEC2017 basic functions. by Tengfei Ma (597633)

Subjects:

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

“…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …”

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …”

Scheduling time of five algorithms. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

Convergence speed of five algorithms. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. …”

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

CEC2017 test function test results. by Tengfei Ma (597633)

Subjects:

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

Subjects:

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Knowledge graph model rating function. by Chunjuan Li (7890182)

Iteration curves of different algorithms. by Tengfei Ma (597633)

Subjects: