176 research outputs found
Interfacial Interaction Enhanced Rheological Behavior in PAM/CTAC/Salt Aqueous Solution—A Coarse-Grained Molecular Dynamics Study
Interfacial interactions within a multi-phase polymer solution play critical roles in processing control and mass transportation in chemical engineering. However, the understandings of these roles remain unexplored due to the complexity of the system. In this study, we used an efficient analytical method—a nonequilibrium molecular dynamics (NEMD) simulation—to unveil the molecular interactions and rheology of a multiphase solution containing cetyltrimethyl ammonium chloride (CTAC), polyacrylamide (PAM), and sodium salicylate (NaSal). The associated macroscopic rheological characteristics and shear viscosity of the polymer/surfactant solution were investigated, where the computational results agreed well with the experimental data. The relation between the characteristic time and shear rate was consistent with the power law. By simulating the shear viscosity of the polymer/surfactant solution, we found that the phase transition of micelles within the mixture led to a non-monotonic increase in the viscosity of the mixed solution with the increase in concentration of CTAC or PAM. We expect this optimized molecular dynamic approach to advance the current understanding on chemical–physical interactions within polymer/surfactant mixtures at the molecular level and enable emerging engineering solutions
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Evolution mechanism of principal modes in climate dynamics
Eigen analysis has been a powerful tool to distinguish multiple processes into different simple principal modes in complex systems. For a non-equilibrium system, the principal modes corresponding to the non-equilibrium processes are usually evolving with time. Here, we apply the eigen analysis into the complex climate systems. In particular, based on the daily surface air temperature in the tropics (30? S–30? N, 0? E–360? E) between 1979-01-01 and 2016-12-31, we uncover that the strength of two dominated intra-annual principal modes represented by the eigenvalues significantly changes with the El Niño/southern oscillation from year to year. Specifically, according to the ‘regional correlation’ introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El Niño region and the northern (southern) hemisphere usually signals the beginning (end) of the El Niño. We discuss the underlying physical mechanism and suppose that the evolution of the first intra-annual principal mode is related to the meridional circulations; the evolution of the second intra-annual principal mode responds positively to the Walker circulation. Our framework presented here not only facilitates the understanding of climate systems but also can potentially be used to study the dynamical evolution of other natural or engineering complex systems. © 2020 The Author(s)
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
The generative adversarial imitation learning (GAIL) has provided an
adversarial learning framework for imitating expert policy from demonstrations
in high-dimensional continuous tasks. However, almost all GAIL and its
extensions only design a kind of reward function of logarithmic form in the
adversarial training strategy with the Jensen-Shannon (JS) divergence for all
complex environments. The fixed logarithmic type of reward function may be
difficult to solve all complex tasks, and the vanishing gradients problem
caused by the JS divergence will harm the adversarial learning process. In this
paper, we propose a new algorithm named Wasserstein Distance guided Adversarial
Imitation Learning (WDAIL) for promoting the performance of imitation learning
(IL). There are three improvements in our method: (a) introducing the
Wasserstein distance to obtain more appropriate measure in the adversarial
training process, (b) using proximal policy optimization (PPO) in the
reinforcement learning stage which is much simpler to implement and makes the
algorithm more efficient, and (c) exploring different reward function shapes to
suit different tasks for improving the performance. The experiment results show
that the learning procedure remains remarkably stable, and achieves significant
performance in the complex continuous control tasks of MuJoCo.Comment: M. Zhang and Y. Wang contribute equally to this wor
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Offline reinforcement learning (RL) has attracted much attention due to its
ability in learning from static offline datasets and eliminating the need of
interacting with the environment. Nevertheless, the success of offline RL
relies heavily on the offline transitions annotated with reward labels. In
practice, we often need to hand-craft the reward function, which is sometimes
difficult, labor-intensive, or inefficient. To tackle this challenge, we set
our focus on the offline imitation learning (IL) setting, and aim at getting a
reward function based on the expert data and unlabeled data. To that end, we
propose a simple yet effective search-based offline IL method, tagged SEABO.
SEABO allocates a larger reward to the transition that is close to its closest
neighbor in the expert demonstration, and a smaller reward otherwise, all in an
unsupervised learning manner. Experimental results on a variety of D4RL
datasets indicate that SEABO can achieve competitive performance to offline RL
algorithms with ground-truth rewards, given only a single expert trajectory,
and can outperform prior reward learning and offline IL methods across many
tasks. Moreover, we demonstrate that SEABO also works well if the expert
demonstrations contain only observations. Our code is publicly available at
https://github.com/dmksjfl/SEABO.Comment: To appear in ICLR202
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