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Found 6 papers, 3 papers with code
Near-optimal control of dynamical systems with neural ordinary differential equations
Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state $\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time $T$.
Control of Dual-Sourcing Inventory Systems using Recurrent Neural Networks
To solve such optimization problems, inventory managers need to decide what quantities to order from each supplier, given the net inventory and outstanding orders, so that the expected backlogging, holding, and sourcing costs are jointly minimized.
Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations
Sustainable consumption aims to minimize the environmental and societal impact of the use of services and products.
Implicit energy regularization of neural ordinary-differential-equation control
Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable.
Neural Ordinary Differential Equation Control of Dynamics on Graphs
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs).
How Value-Sensitive Design Can Empower Sustainable Consumption
In a so-called overpopulated world, sustainable consumption is of existential importance. However, the expanding spectrum of product choices and their production complexity challenge consumers to make informed and value-sensitive decisions.
