AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Topology optimization (TO) represents a significant step towards automating the design process: given a working simulation, TO can produce a viable prototype at the press of a button by differentiating the simulation and iteratively improving the design. In practice, however, TO is riddled with ``magic numbers''---hype...
Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel training and whose predictions may depend on the discretization of the applied strain path. We introduce a Constitutive State Space (CSS) mode...
Reinforcement Learning to Choose Optimizers is introduced, which formulates the optimization algorithm choice as a sequential decision-making problem and outperforms every portfolio optimizer at all but the smallest budgets.
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