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Wenzhen Huang

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3 papers

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EAAI Journal 2023 Journal Article

Data-driven methods for stress field predictions in random heterogeneous materials

Enjamamul Hoq
Osama Aljarrah
Jun Li
Jing Bi
Alfa Heryudono
Wenzhen Huang

Predicting full-field stress responses is of fundamental importance to assessing materials failure and has various engineering applications in design optimization, manufacturing process control, and structural health monitoring. This article develops and evaluates different data-driven methods for efficient and accurate predictions of full stress fields in random heterogeneous materials. The first approach integrates model order reduction of proper orthogonal decomposition (POD) with classical machine learning techniques (K-nearest neighbors, random forest, and artificial neural networks) to predict full-field responses based on POD-reduced coefficients. However, this strategy shows limitations in predicting full stress fields, especially for heterogeneous material inclusions of small size or being close to the domain boundary. After that, two computer vision-based deep learning approaches were developed for full-field predictions. The first one uses a Resnet-based Convolutional Neural Network (CNN), and the second is based on a modified conditional Generative Adversarial Network (cGAN). Two representative example problems were studied: a random heterogeneous material inclusion or a void varying in size and location. In contrast to POD-based classical machine learning, almost invisible differences were found between the entire stress fields in finite element simulations and computer vision-based deep learning (CNN/cGAN) predictions, with significantly reduced mean squared error (MSE) and correlation values (R2) mostly above 0. 99. On the other hand, the proposed cGAN provides more accurate predictions than CNN with fewer epochs.

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AAAI Conference 2021 Conference Paper

Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning

Wenzhen Huang
Qiyue Yin
Junge Zhang
Kaiqi Huang

Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories may be deleterious for training the action-value and policy functions. To alleviate such problem, this paper proposes to adaptively reweight the imaginary transitions, so as to reduce the negative effects of poorly generated trajectories. More specifically, we evaluate the effect of an imaginary transition by calculating the change of the loss computed on the real samples when we use the transition to train the action-value and policy functions. Based on this evaluation criterion, we construct the idea of reweighting each imaginary transition by a well-designed meta-gradient algorithm. Extensive experimental results demonstrate that our method outperforms state-of-the-art model-based and model-free RL algorithms on multiple tasks. Visualization of our changing weights further validates the necessity of utilizing reweight scheme.

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AAAI Conference 2019 Conference Paper

Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning

Wenzhen Huang
Junge Zhang
Kaiqi Huang

Model-based reinforcement learning (RL) methods attempt to learn a dynamics model to simulate the real environment and utilize the model to make better decisions. However, the learned environment simulator often has more or less model error which would disturb making decision and reduce performance. We propose a bootstrapped model-based RL method which bootstraps the modules in each depth of the planning tree. This method can quantify the uncertainty of environment model on different state-action pairs and lead the agent to explore the pairs with higher uncertainty to reduce the potential model errors. Moreover, we sample target values from their bootstrap distribution to connect the uncertainties at current and subsequent time-steps and introduce the prior mechanism to improve the exploration efficiency. Experiment results demonstrate that our method efficiently decreases model error and outperforms TreeQN and other stateof-the-art methods on multiple Atari games.

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