Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses⁴.

物理系统科学模拟中的几个应用可以表述为控制/优化问题。此类系统的计算模型通常包含控制解决方案保真度和计算费用的超参数。这些参数的调整非常重要，一般的方法是手动“抽查”以获得良好的组合。这是因为当参数空间很大并且它们可能会动态变化时，最佳超参数配置搜索变得棘手。为了解决这个问题，我们提出了一个基于深度强化学习 (RL) 的框架来训练一个深度神经网络代理，该代理通过动态改变参数来控制模型求解。第一的，我们通过动态改变系统的参数来验证我们的 RL 框架在混沌系统中控制混沌的问题。随后，我们说明了我们的框架通过在运行时自动调整离散 Navier-Stokes 方程的松弛因子来加速稳态计算流体动力学求解器收敛的能力。结果表明，与松弛因子的随机选择相比，学习策略对松弛因子的运行时控制导致收敛迭代次数显着减少。我们的结果指出了学习跨不同几何形状和边界条件的自适应超参数学习策略的潜在好处，这对减少计算活动费用有影响 我们通过在运行时自动调整离散 Navier-Stokes 方程的松弛因子来说明我们的框架加速稳态计算流体动力学求解器收敛的能力。结果表明，与松弛因子的随机选择相比，学习策略对松弛因子的运行时控制导致收敛迭代次数显着减少。我们的结果指出了学习跨不同几何形状和边界条件的自适应超参数学习策略的潜在好处，这对减少计算活动费用有影响 我们通过在运行时自动调整离散 Navier-Stokes 方程的松弛因子来说明我们的框架加速稳态计算流体动力学求解器收敛的能力。结果表明，与松弛因子的随机选择相比，学习策略对松弛因子的运行时控制导致收敛迭代次数显着减少。我们的结果指出了学习跨不同几何形状和边界条件的自适应超参数学习策略的潜在好处，这对减少计算活动费用有影响⁴ .