AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields

We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.

WindDragon: Enhancing wind power forecasting with Automated Deep Learning

Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) windpower forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

Interpretable time series neural representation for classification purposes

Deep learning has made significant advances in creating efficient representations of time series data by automatically identifying complex patterns. However, these approaches lack interpretability, as the time series is transformed into a latent vector that is not easily interpretable. On the other hand, Symbolic Aggregate approximation (SAX) methods allow the creation of symbolic representations that can be interpreted but do not capture complex patterns effectively. In this work, we propose a set of requirements for a neural representation of univariate time series to be interpretable. We propose a new unsupervised neural architecture that meets these requirements. The proposed model produces consistent, discrete, interpretable, and visualizable representations. The model is learned independently of any downstream tasks in an unsupervised setting to ensure robustness. As a demonstration of the effectiveness of the proposed model, we propose experiments on classification tasks using UCR archive datasets. The obtained results are extensively compared to other interpretable models and state-of-the-art neural representation learning models. The experiments show that the proposed model yields, on average better results than other interpretable approaches on multiple datasets. We also present qualitative experiments to asses the interpretability of the approach.

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

Although widely explored, time series modeling continues to encounter significant challenges when confronted with real-world data. We propose a novel modeling approach leveraging Implicit Neural Representations (INR). This approach enables us to effectively capture the continuous aspect of time series and provides a natural solution to recurring modeling issues such as handling missing data, dealing with irregular sampling, or unaligned observations from multiple sensors. By introducing conditional modulation of INR parameters and leveraging meta-learning techniques, we address the issue of generalization to both unseen samples and time window shifts. Through extensive experimentation, our model demonstrates state-of-the-art performance in forecasting and imputation tasks, while exhibiting flexibility in handling a wide range of challenging scenarios that competing models cannot.