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A review on Deep Reinforcement Learning for Fluid Mechanics
Deep reinforcement learning (DRL) has recently been adopted in a wide ra...
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Direct shape optimization through deep reinforcement learning
Deep Reinforcement Learning (DRL) has recently spread into a range of do...
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Deep Reinforcement Learning in Portfolio Management
In this paper, we implement two state-of-art continuous reinforcement le...
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Stealing Deep Reinforcement Learning Models for Fun and Profit
In this paper, we present the first attack methodology to extract black-...
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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Black-box optimizers that explore in parameter space have often been sho...
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Accelerating Derivative-Free Optimization with Dimension Reduction and Hyperparameter Learning
We consider convex, black-box objective functions with additive or multi...
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Deep Model Predictive Control with Online Learning for Complex Physical Systems
The control of complex systems is of critical importance in many branche...
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Optimization and passive flow control using single-step deep reinforcement learning
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerate" version of the proximal policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode, and an in-house stabilized finite elements environment implementing the variational multiscale (VMS) method, that computes the numerical reward fed to the neural network. Three prototypical examples of separated flows in two dimensions are used as testbed for developing the methodology, each of which adds a layer of complexity due either to the unsteadiness of the flow solutions, or the sharpness of the objective function, or the dimension of the control parameter space. Relevance is carefully assessed by comparing systematically to reference data obtained by canonical direct and adjoint methods. Beyond adding value to the shallow literature on this subject, these findings establish the potential of single-step PPO for reliable black-box optimization of computational fluid dynamics (CFD) systems, which paves the way for future progress in optimal flow control using this new class of methods.
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