In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Example Input: Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, the uses have been restricted by the fact the training data are obtained using PDE solvers. Thereby, the uses were limited to domains, where the PDE solver was applicable, but no further.

We present methods for training on small domains, while applying the trained models on larger domains, with consistency constraints ensuring the solutions are physically meaningful even at the boundary of the small domains. We demonstrate the results on an air-pollution forecasting model for Dublin, Ireland.
Example Output: We present RNNs for training surrogate models of PDEs, wherein consistency constraints ensure the solutions are physically meaningful, even when the training uses much smaller domains than the trained model is applied to.

Example Input: Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media.
When it comes to visualising these large corpora, either the textual content or the network graph are used.

In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.
Example Output: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.

Example Input: We propose to extend existing deep reinforcement learning (Deep RL) algorithms by allowing them to additionally choose sequences of actions as a part of their policy. This modification forces the network to anticipate the reward of action sequences, which, as we show, improves the exploration leading to better convergence. Our proposal is simple, flexible, and can be easily incorporated into any Deep RL framework. We show the power of our scheme by consistently outperforming the state-of-the-art GA3C algorithm on several popular Atari Games.
Example Output:
Anticipation improves convergence of deep reinforcement learning.