During the design process of an autonomous underwater vehicle (AUV), the
pressure vessel has a critical role. The pressure vessel contains dry
electronics, power sources, and other sensors that can not be flooded. A
traditional design approach for a pressure vessel design involves running
multiple Finite Element Analysis (FEA) based simulations and optimizing the
design to find the best suitable design which meets the requirement. Running
these FEAs are computationally very costly for any optimization process and it
becomes difficult to run even hundreds of evaluation. In such a case, a better
approach is the surrogate design with the goal of replacing FEA-based
prediction with some learning-based regressor. Once the surrogate is trained
for a class of problem, then the learned response surface can be used to
analyze the stress effect without running the FEA for that class of problem.
The challenge of creating a surrogate for a class of problems is data
generation. Since the process is computationally costly, it is not possible to
densely sample the design space and the learning response surface on sparse
data set becomes difficult. During experimentation, we observed that a Deep
Learning-based surrogate outperforms other regression models on such sparse
data. In the present work, we are utilizing the Deep Learning-based model to
replace the costly finite element analysis-based simulation process. By
creating the surrogate we speed up the prediction on the other design much
faster than direct Finite element Analysis. We also compared our DL-based
surrogate with other classical Machine Learning (ML) based regression models(
random forest and Gradient Boost regressor). We observed on the sparser data,
the DL-based surrogate performs much better than other regression models.

本文提出使用深度学习代替传统有限元分析来设计自主水下车压力容器，并证明在稀疏数据集情况下，深度学习代理比其他回归模型更优。

基于深度学习的海底压力容器有限元分析（FEA）代理

Deep Learning-based Finite Element Analysis (FEA) surrogate for sub-sea pressure vessel

We present an algorithm for rapidly learning controllers for robotics
systems. The algorithm follows the model-based reinforcement learning paradigm,
and improves upon existing algorithms; namely Probabilistic learning in Control
(PILCO) and a sample-based version of PILCO with neural network dynamics
(Deep-PILCO). We propose training a neural network dynamics model using
variational dropout with truncated Log-Normal noise. This allows us to obtain a
dynamics model with calibrated uncertainty, which can be used to simulate
controller executions via rollouts. We also describe set of techniques,
inspired by viewing PILCO as a recurrent neural network model, that are crucial
to improve the convergence of the method. We test our method on a variety of
benchmark tasks, demonstrating data-efficiency that is competitive with PILCO,
while being able to optimize complex neural network controllers. Finally, we
assess the performance of the algorithm for learning motor controllers for a
six legged autonomous underwater vehicle. This demonstrates the potential of
the algorithm for scaling up the dimensionality and dataset sizes, in more
complex control tasks.

本研究提出了一种基于模型的强化学习算法，通过训练具有可校准不确定性的神经网络动力学模型，实现对机器人系统控制器的快速学习，并在多项基准任务中验证了该算法的高效性和可扩展性，包括针对六足水下自主机器人的运动控制器学习。