GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
Multilayer feedforward networks are universal approximators.Neural Networks, 2(5):359–366, 1989
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Neural network emulators of Grad-Shafranov equilibria enable real-time derivation of virtual circuits that disentangle plasma shape control parameters in tokamaks.
citing papers explorer
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GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization
GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
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Real-time virtual circuits for plasma shape control via neural network emulators
Neural network emulators of Grad-Shafranov equilibria enable real-time derivation of virtual circuits that disentangle plasma shape control parameters in tokamaks.