DCIDA uses single-step reinforcement learning with a deterministic action-to-layout mapping to generate distributed resonator circuits that match target transfer functions more closely than prior methods on a surrogate-based benchmark.
Parametric modeling of microwave components using adjoint neural networks and pole-residue transfer functions with em sensitivity analysis
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Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
DCIDA uses single-step reinforcement learning with a deterministic action-to-layout mapping to generate distributed resonator circuits that match target transfer functions more closely than prior methods on a surrogate-based benchmark.